About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.

This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.

What You'll Do

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform
• Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production
• Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds
• Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
• Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability 
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have a strong interest in LLM serving; prior inference or ML experience is not required 
• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Strong Candidates May Also Have Experience With:

• LLM inference optimization, batching, and caching strategies
• Capacity-constrained scheduling or shared-resource test infrastructure
• Solid understanding of multi-region deployments, request routing, load balancing, global traffic management
• Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service
• Proficiency in Python or Rust

The Security Products organization at CoreWeave builds the identity, encryption, and self-managed security integrations that protect AI workloads and data across our cloud platform. If you are passionate about building foundational security primitives that enable enterprises and the top AI labs in the world to deploy regulated and security‑sensitive workloads at extreme scale, this is the team to join!

About the role

CoreWeave is seeking a Staff or Principal Engineer for our Security Products team to lead the technical direction and implementation of encryption and key lifecycle management  In this role, you’ll design and evolve the key lifecycle management, encryption control planes, algorithm/library selection, and systems integrations that allow CoreWeave customers to deploy sensitive, high security AI workloads and data. You’ll partner closely with teams across CoreWeave to develop customer-driven cryptography technology. Your day‑to‑day will blend hands‑on system design and coding with cross‑team technical leadership, design reviews, and roadmap shaping for Security Products.

In this role, you will:

• Lead the design and evolution of encryption and key lifecycle management products.
• Manage encryption and cryptography technology development for services within our Cloud Platform, particularly those for high security and highly regulated customers.
• Design and build deep integrations between our Cloud Platform and external key sources (eg, HashiCorp Vault, AWS KMS, HSMs).
• Collaborate with other product engineering teams to support the safe use of multicloud key management technology.
• Partner with IAM to define unified authorization patterns and policy models for key management APIs with consistent semantics across the resource hierarchy.
• Establish SLIs / SLOs for Remote Key Encryption (RKE) and related services, including availability, latency, and durability guarantees for key retrieval and encryption operations.
• Partner with the Security Engineering team on threat modeling and corporate strategy to enable the most sensitive AI workloads in the world to be deployed on CoreWeave's infrastructure.
• Author and review detailed technical designs and RFCs for new RKE capabilities, mentor other engineers on the team, and provide technical leadership across Security Products and adjacent organizations.

Who You Are

• 8+ years of experience building and operating distributed backend systems in production, including ownership of reliability and security outcomes for critical services.
• Deep experience with encryption at rest and key management systems, including envelope encryption patterns, key hierarchies and secure key lifecycle management.
• Hands-on experience integrating with at least one major KMS or secrets manager (e.g., AWS KMS, HashiCorp Vault, Azure Key Vault, GCP KMS, HSMs), including designing APIs and workflows around those systems.
• Strong proficiency in a systems programming language such as Go (preferred) or Rust, with experience building networked services (gRPC / REST) in a Linux / Kubernetes environment.
• Solid understanding of applied cryptography concepts relevant to data‑at‑rest protection (AES‑GCM/CTR, key wrapping, KDFs, randomness requirements, envelope encryption, and key separation) with the ability to reason about threat models and failure modes with Security partners.
• Experience designing and operating multi‑tenant services with strong isolation and authorization semantics across customers and internal tenants.
• Demonstrated track record of leading cross‑team technical initiatives, driving projects from problem statement through rollout, alignment, and operational readiness.
• Strong operational experience defining SLIs / SLOs, building dashboards and alerts, and partnering with SRE / Production Engineering on incident response and post‑incident improvement.
• Excellent written and verbal communication skills with the ability to produce clear, opinionated design docs that influence Senior Engineers, PMs, and Security stakeholders through context setting and sound technical judgment

Preferred (if applicable)

• Prior experience designing or implementing remote or externalized key management for cloud storage, databases, or filesystems (e.g., BYOK/BYOKMS, customer‑managed keys, envelope encryption for S3‑like object storage).
• Experience with hardware‑backed key management (HSMs) and cryptographic compliance regimes (FIPS 140‑2/3, PCI, HIPAA, FedRAMP Moderate+, or similar) and how they shape system design.
• Familiarity with IAM policy models (RBAC / ABAC, OpenFGA, OPA/Rego, etc.) and how to integrate fine‑grained authorization into security‑sensitive APIs.
• Experience extending encryption and key management across multiple storage domains (object storage, block/file storage, databases, control plane state like etcd) in a coherent way.
• Background working in security‑sensitive or regulated environments where auditability, segregation of duties, and key custody requirements are critical.
• Contributions to open source cryptography, security tooling, or KMS/client libraries.
• Previous US/NATO federal cryptographic security experience is ideal but not necessary. 

Wondering if you’re a good fit?

We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You care deeply about getting the cryptographic and operational details right, and you’re comfortable saying “no” to shortcuts that weaken security or key custody guarantees.
• You’re excited by the challenge of building foundational security primitives that other product teams and large enterprises will build on for years.
• You enjoy working at the intersection between Platform, Storage, IAM, and Security Engineering, and you can translate between those domains without losing the plot.
• You’re skilled at turning complex requirements for highly regulated customers into simple, reliable, well‑documented APIs and workflows.
• You’re comfortable operating in a fast‑moving environment, iterating quickly while still holding a high bar for design review, testing, and safe rollout of security‑sensitive changes.

The base salary range for this role is $206,000 to $303,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

MongoDB is a complex, mission-critical database server that operates in a wide range of environments. Customers rely on its ability to meet high standards for Performance, Security, Availability, and Correctness. Achieving these standards requires robust developer tooling for every layer of the stack, from the underlying C++ storage engine through our Atlas Cloud offering. The Developer Productivity organization plays a vital role in enabling MongoDB engineers to ship products that meet our high company standards.

The Build Team focuses on ensuring that our code is able to be packaged and shipped for all supported operating systems and architectures. We are responsible for ensuring that this build happens consistently, quickly, and correctly, and that we do as much as possible to ensure performance and correctness of the resulting binaries. Tooling changes can have a substantial impact on the final product we ship to customers, with tooling changes recently yielding a 10% improvement in the overall performance of the MongoDB database. Under the hood we use the Bazel build system in order to meet these goals and to handle the multi-language nature of code shipped at MongoDB. 

We are looking to speak to candidates who are based in New York City for our hybrid working model.

The ideal candidate should

• Have completed a relevant software development internship or similar summer research experience
• Have experience and interest in using and tuning AI tools to accelerate development velocity and improve code quality
• Be interested in learning about build systems such as Bazel and assembling and shipping highly complex software at scale
• Be interested in working specifically with C++, Rust, Python, and Java
• Demonstrate the ability to quickly learn, adapt to, and excel with new technologies, frameworks, and tools in dynamic environments

Expectations

• Update our tooling to improve the reliability, performance, and developer experience of the systems used every day by developers, locally and in our continuous integration environments
• Provide internal support for our systems, answering questions and addressing technical issues as they arise
• Learn about core software development principles around shipping code at scale, and grow your ability to contribute to the overall operational excellence of our build systems
• Demonstrate a strong empathy for our internal customers

Success

Three Months:

After three months, you will have made contributions to in-progress software development projects. You will be able to reliably work on small features and bugs. You will be expected to start looking for medium sized features and projects of approximately 2 weeks of effort.

Six Months:

You will have working knowledge of many of our build systems and be able to consistently deliver medium sized projects and features with low supervision in some areas, interfacing with customers and stakeholders. You will be suggesting appropriate smaller fixes and improvements to our codebase, and be able to provide input on smaller projects and features proposed by other members of the team.

One Year:

You will regularly be making contributions to technical projects that have a meaningful impact on our ability to reliably deliver a good experience for the developers that use our products at MongoDB. You will have strong knowledge of areas of our code that you have worked in, and will be able to provide some guidance on technical directions, with support from senior engineers.

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req. ID: 1273421176

As a Software Engineer on the ML Infrastructure team, you will design and build platforms for scalable, reliable, and efficient serving of LLMs. Our platform powers cutting-edge research and production systems, supporting both internal and external use cases across various environments.

The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging research and engineering to deliver seamless experiences to our customers and accelerate innovation across the company.

You will:

• Build and maintain fault-tolerant, high-performance systems for serving LLMs workloads at scale.
• Build an internal platform to empower LLM capability discovery.
• Collaborate with researchers and engineers to integrate and optimize models for production and research use cases.
• Conduct architecture and design reviews to uphold best practices in system design and scalability.
• Develop monitoring and observability solutions to ensure system health and performance.
• Lead projects end-to-end, from requirements gathering to implementation, in a cross-functional environment. 

Ideally you'd have:

• 5+ years of experience building large-scale, high-performance backend systems.
• Strong programming skills in one or more languages (e.g., Python, Go, Rust, C++).
• Experience with LLM serving and routing fundamentals (e.g. rate limiting, token streaming, load balancing, budgets, etc.)
• Experience with LLM capabilities and concepts such as reasoning, tool calling, prompt templates, etc.
• Experience with containers and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference.

As a Software Engineer on the Machine Learning Infrastructure team, you will build the "Operating System" for our large-scale GPU clusters. You will architect a high-performance training platform that handles the immense complexity of multi-thousand GPU workloads, ensuring every cycle is used efficiently. Your work directly determines the velocity at which our researchers can train and iterate on the world’s most advanced models.

The ideal candidate is a systems expert who thrives on solving the orchestration, networking, and reliability challenges that emerge at massive scale. You will partner closely with researchers to build a seamless, resilient environment that transforms raw compute into breakthrough AI.

You will:

• Architect and scale a multi-tenant orchestration layer that abstracts away the complexity of GPU clusters, ensuring high utilization and seamless job recovery.
• Design and implement scheduling primitives to optimize the lifecycle of training jobs.
• Develop deep observability and automated health-checking into the training stack to proactively identify and isolate hardware failures
• Evaluate and integrate emerging technologies in the CNCF and AI ecosystem (e.g. Ray, Kueue), making data-driven build vs. buy decisions that balance velocity with long-term maintainability.
• Work closely with Finance and Procurement teams to drive our capacity planning process.
• Participate in our team’s on call process to ensure the availability of our services.
• Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment.

Ideally you'd have:

• 5+ years of experience in backend or infrastructure engineering, with at least 2 years focused on orchestrating ML workloads at scale (100+ GPU nodes).
• Strong programming skills in one or more languages (e.g. Python, Go, Rust, C++)
• Experience with complex compute management systems that cover queueing, quotas, preemption, and gang scheduling.
• Experience with distributed training infrastructure, such as EFA, Infiniband, and topology-aware scheduling.
• Experience with distributed storage systems (e.g. Lustre, S3) as they relate to training throughput
• Expert-level knowledge of Kubernetes internals (Custom Resources, Operators, Admission Controllers) and how they interact with device plugins for specialized hardware.
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with distributed training techniques such as DeepSpeed, FSDP, etc.
• Experience with the NVIDIA software and hardware stack (CUDA, NCCL)
• Experience with PyTorch
• Familiarity with post-training algorithms such as GRPO, and with Reinforcement Learning

About the Role

Anthropic manages one of the largest and fastest-growing accelerator fleets in the industry — spanning multiple accelerator families and clouds. The Accelerator Capacity Engineering (ACE) team is responsible for making sure every chip in that fleet is accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute.

As an engineer on ACE, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend.

You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment.

What This Team Owns

The team’s work spans three functional areas. Depending on your background and interests, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap:

• Data infrastructure — collecting, normalizing, and serving the fleet-wide data that powers everything else. This means building pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage data across cloud providers, and maintaining the BigQuery layer that the rest of the org queries against. Correctness, completeness, and latency matter here.

• Fleet observability — making the state of the accelerator fleet legible and actionable in real time. This means building cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and driving systemic improvements to scheduling and fragmentation. The work sits at the intersection of Kubernetes operations and cross-team coordination.

• Compute efficiency — measuring and improving how effectively every major workload uses the hardware it’s running on. This means instrumenting utilization metrics across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and collaborating directly with system-owning teams to close efficiency gaps.

• Internal compute tooling — building the platforms and interfaces that make capacity data usable across the org. This includes capacity planning tools, workload attribution systems, cost dashboards, and self-service APIs. The consumers are research engineers, infrastructure teams, finance, and leadership — each with different needs and different levels of technical depth. The work involves product thinking as much as engineering: figuring out what people actually need, defining schema contracts, and making the data discoverable.

You will be placed on a pod based on your background and interests. We are especially focused on hiring for Data Platform, but strong candidates for any of the three active pods will move forward.

What You’ll Do

• Build and operate data pipelines that ingest accelerator occupancy, utilization, and cost data from multiple cloud providers into BigQuery. Own data completeness, latency SLOs, gap detection, and backfill automation.

• Develop and maintain observability infrastructure — Prometheus recording rules, Grafana dashboards, and alerting systems — that surface actionable signals about fleet health, occupancy, and efficiency.

• Instrument and analyze compute efficiency metrics across training, inference, and eval workloads. Build benchmarking infrastructure, establish per-config baselines, and work with system-owning teams to improve utilization.

• Build internal tooling and platforms that enable capacity planning, workload attribution, and cluster debugging. The consumers are other engineering teams, finance, and leadership — not external users.

• Operate Kubernetes-native systems at scale — deploying data collection agents, managing workload labeling infrastructure, and understanding how taints, reservations, and scheduling affect capacity.

• Normalize and reconcile data across heterogeneous sources — including AWS, GCP, and Azure billing exports, vendor-specific telemetry formats, and internal systems with different schemas and billing arrangements.

• Collaborate across organizational boundaries with research engineering, infrastructure, inference, and finance teams. Gather requirements from technical stakeholders, translate them into useful systems, and communicate trade-offs to non-technical audiences.

You May Be a Good Fit If You Have

• 5+ years of software engineering experience with a strong track record building and operating production systems. You write code every day — this is a hands-on engineering role, not a planning or coordination role.

• Kubernetes fluency at operational depth — you’ve operated production K8s at meaningful scale, not just written manifests. Comfort with scheduling, taints, labels, node management, and debugging cluster-level issues.

• Data pipeline engineering experience — designing, building, and owning the full lifecycle of production data pipelines. Experience with data warehouses (BigQuery preferred), schema management, streaming ingestion, SLOs for latency and completeness, and a strong instinct for correctness.

• Observability tooling experience — Prometheus, PromQL, and Grafana are in the critical path for this team. Experience writing recording rules, understanding metric semantics, and building monitoring systems that engineering teams actually rely on.

• Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable.

• Familiarity with at least one major cloud provider (AWS, GCP, or Azure) at the infrastructure level — compute, billing, usage APIs, cost management tooling. Multi-cloud experience is a strong plus.

• High autonomy and strong cross-team communication. You can gather your own requirements, navigate ambiguity, and work across organizational boundaries. Scrappiness and ownership matter more than polish.

Strong Candidates May Also Have

• Multi-cloud data ingestion experience — especially working with AWS and GCP APIs, billing exports, or vendor-specific telemetry formats. Experience normalizing data from external providers with different billing arrangements is directly applicable.

• Accelerator infrastructure familiarity — GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience working with ML training/inference systems at the hardware level.

• Performance engineering and benchmarking experience — building benchmark harnesses, establishing baselines, reasoning about compute efficiency (FLOPs utilization, memory bandwidth, interconnect throughput), and working with system teams to diagnose and improve performance.

• Data-as-product thinking — experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just building pipelines, but thinking about how platform data gets consumed.

• Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or large-scale ML environment. FinOps, chargeback systems, or infrastructure cost modeling.

Familiarity with ClickHouse, Terraform, or Rust. ClickHouse is the team’s current streaming store; Terraform for infrastructure-as-code; Rust for high-performance data collection agents.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

You will join the global Security function, which is responsible for safeguarding QRT’s technology platforms, data, and operational environments. The team partners closely with Engineering, Infrastructure, Risk, Compliance, and business stakeholders to design and implement security controls, reduce risk, and enable safe, high-performance operations across global offices.

Your future role within QRT

• Support the implementation of security controls and processes for product security, focusing on a broad range of systems, including core trading infrastructure, cloud services, and business applications across both Windows and Linux environments.
• Collaborate with engineering and product teams to integrate security into product design and development, applying your experience in securing large-scale software systems in a fast-moving environment.
• Contribute to the development and maintenance of a secure software development lifecycle (SDLC) with a focus on secure coding practices in languages like Python, C++, Rust, Go and Kotlin/Java.
• Conduct threat modeling, vulnerability assessments and security code reviews across different platforms, ensuring security is embedded at every stage of the development lifecycle.
• Provide mentorship, guidance, and training on security best practices and secure development processes to engineering teams working in mixed cloud and operating systems environments.
• Perform vendor security reviews to assess third-party security practices and ensure compliance with QRT’s standards.
• Integration of security scanning tools (SAST, DAST, etc.) into CI/CD pipelines and runtime environments to ensure continuous security monitoring and threat detection across Alibaba Cloud, AWS, Azure, and on-prem systems.
• Proactively identify security risks and develop strategies for risk mitigation in a fast-paced high-stakes environment.

Your present skillset

• At least 7 years of experience in product security or similar roles with significant practical experience in securing software development at scale.
• Proven record of accomplishment in secure coding practices and development experience in development languages such as Python, C++, Rust, Go and Kotlin/Java.
• Strong technical background in software development, system architecture and security tools.
• Strong understanding of security principles, techniques and technologies related to software and product security, cloud platforms and business applications. Knowledge of low-latency financial systems would be an advantage.
• Experience working with and securing both Windows and Linux-based systems.
• Extensive experience with one or more cloud platforms such as AWS, Microsoft Azure and Alibaba Cloud used in a hybrid environment.
• In-depth knowledge of threat modeling, risk assessment and development of mitigation strategies for large-scale, complex systems in a fast-paced environment.
• Experience integrating security scanning tools into CI/CD pipelines and runtime environments.
• Experience conducting vendor security reviews and managing third-party security assessments.
• Excellent leadership, problem-solving, communication and adaptability skills, suited for a senior-level position in a fast-paced environment.

Base salary range for this position is $160,000 to $220,000 per year. 

QRT Total Compensation includes discretionary performance-based bonuses and a competitive benefits package.

We are hiring a Senior Software Engineer to join our Server Security team. The Server Security team is a development-focused group within MongoDB's core engineering organization. Operating "close to the bottom of the stack," the team builds features that enable database users to secure their data globally. You will work on critical components including:

• Cryptography: Queryable Encryption, at-rest data encryption, and fundamental cryptographic principles.
• Identity & Access: Authentication and authorization systems, TLS, and X.509 certificate management
• Network Security: High-performance, low-latency networking protocols (PKI, Hashing, CRLs)
• System Integrity: Resilience, observability, and compliance assurance within a large-scale distributed database

Our team champions a strong culture of inclusivity, diversity, and collaboration. If you want to work on a collaborative team that applies distributed systems fundamentals to deliver core features of a popular database, join us! Let’s change what’s possible for application developers, system architects, and database operators.

The role

As a Senior Engineer, you will apply distributed systems fundamentals to deliver core security features. You will be a leader in improving MongoDB's security posture by owning features and leading investigations into complex areas of the codebase. What you’ll do:

• Build and test new security features in a large, feature-rich C++ codebase
• Work across engineering, cloud services, and support teams to coordinate feature rollouts and changes
• Stand for code quality and security best practices, assisting fellow engineers in writing well-reasoned, secure code
• Use strong diagnostic intuition to solve thorny technical issues related to distributed systems, concurrency, and OS internals

This role can be remote or hybrid anywhere in the USA or Canada. We will prioritize candidates who are already located in one of these countries.

Candidate Profile

We are looking for a highly technical engineer who thrives on the theory and practice of large-scale distributed system design.

• 5+ years of experience building distributed systems
• Proficiency in a compiled language (C++, Rust, Go, Java, or similar). Technical interviews will be conducted in a compiled language of your choice
• Ability to reason about how specific design choices impact a distributed system like MongoDB
• Understands modern computer architecture and internals to the degree that they can reason about how specific design choices plays out in a system like MongoDB
• Degree in Computer Science or similar field, or equivalent practical experience, with strong competencies in data structures, algorithms, and software design/architecture
• Excellent communication skills to synthesize complex technical implications for diverse stakeholders

Success Measures

• In your first month, you’ll understand the high-level architecture of MongoDB, dive into the details of your team’s components, and resolve a few bugs
• In the first three months, you’ll contribute to the development of a project slated for the next release of MongoDB, take on code review responsibilities, and participate in design reviews for new features
• In the first six months, you’ll lead the development of a new feature and help mentor new engineers on the team

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 1273412835

We are hiring a Senior Software Engineer to join our Server Security team. The Server Security team is a development-focused group within MongoDB's core engineering organization. Operating "close to the bottom of the stack," the team builds features that enable database users to secure their data globally. You will work on critical components including:

• Cryptography: Queryable Encryption, at-rest data encryption, and fundamental cryptographic principles.
• Identity & Access: Authentication and authorization systems, TLS, and X.509 certificate management
• Network Security: High-performance, low-latency networking protocols (PKI, Hashing, CRLs)
• System Integrity: Resilience, observability, and compliance assurance within a large-scale distributed database

Our team champions a strong culture of inclusivity, diversity, and collaboration. If you want to work on a collaborative team that applies distributed systems fundamentals to deliver core features of a popular database, join us! Let’s change what’s possible for application developers, system architects, and database operators.

The role

As a Senior Engineer, you will apply distributed systems fundamentals to deliver core security features. You will be a leader in improving MongoDB's security posture by owning features and leading investigations into complex areas of the codebase. What you’ll do:

• Build and test new security features in a large, feature-rich C++ codebase
• Work across engineering, cloud services, and support teams to coordinate feature rollouts and changes
• Stand for code quality and security best practices, assisting fellow engineers in writing well-reasoned, secure code
• Use strong diagnostic intuition to solve thorny technical issues related to distributed systems, concurrency, and OS internals

This role can be remote or hybrid anywhere in the USA or Canada. We will prioritize candidates who are already located in one of these countries.

Candidate Profile

We are looking for a highly technical engineer who thrives on the theory and practice of large-scale distributed system design.

• 5+ years of experience building distributed systems
• Proficiency in a compiled language (C++, Rust, Go, Java, or similar). Technical interviews will be conducted in a compiled language of your choice
• Ability to reason about how specific design choices impact a distributed system like MongoDB
• Understands modern computer architecture and internals to the degree that they can reason about how specific design choices plays out in a system like MongoDB
• Degree in Computer Science or similar field, or equivalent practical experience, with strong competencies in data structures, algorithms, and software design/architecture
• Excellent communication skills to synthesize complex technical implications for diverse stakeholders

Success Measures

• In your first month, you’ll understand the high-level architecture of MongoDB, dive into the details of your team’s components, and resolve a few bugs
• In the first three months, you’ll contribute to the development of a project slated for the next release of MongoDB, take on code review responsibilities, and participate in design reviews for new features
• In the first six months, you’ll lead the development of a new feature and help mentor new engineers on the team

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

Req ID: 1273412835

We are hiring a Senior Lead Engineer to manage the largely New York-based Storage Execution team. This is a hands-on leadership role for someone who is obsessed with customer success, takes the responsibility of durably storing customer data seriously, and has strong empathy for both their team and our users.

About the Storage Execution team

The Storage Execution team sits at the heart of MongoDB’s Replicated Storage Services (RSS). We own the core transactional read/write path on each node, across both today’s storage engine and a next‑generation cloud-native storage architecture. Our work determines how correct and performant MongoDB is for customers running mission-critical production workloads.

The team’s responsibilities include:

• Defining and maintaining the internal collection and index APIs used by Replication, Query and Catalog modules, and commands for local reads and writes, plus the transaction and durability primitives that govern visibility, conflict resolution and crash recovery.  
• Integrating the next generation of MongoDB’s storage architecture into the server in a way that is predictable for other teams, scales with cloud elasticity, and preserves the performance and safety of the classic configuration.  
• Owning index builds and schema‑level storage operations (build/rebuild, cleanup, resumability) and ensuring they behave well for large, highly available clusters running write-heavy workloads.  
• Building validation, diagnostics, and storage‑focused test infrastructure to catch durability issues early and keep the system trustworthy at scale.  
• Partnering with neighboring teams on flow control, prioritization and load-shedding to stay within resource limits, protecting durability and availability at the cluster level without starving critical operations.

What you’ll do

• Lead, grow and manage a New York–based team of 8 to 10 engineers; ensure clear expectations, high standards, and healthy, sustainable execution
• Own the team’s roadmap across durability, availability, performance, and developer productivity, in partnership with RSS leadership and Product Management
• Manage customer escalations involving storage behavior and durability; work directly with Support, Atlas, and other server teams to reproduce, root‑cause, and resolve issues
• Drive process and tooling improvements that raise engineering velocity and code quality (e.g., ownership, review expectations, HELP/BF practices)
• Partner closely with Replication, Query, Sharding, Catalog & Routing, Storage Engines, and Atlas teams on cross‑team features such as the new storage architecture, availability improvements, and workload management
• Represent Storage Execution in planning, staffing, and calibration; make thoughtful tradeoffs between near‑term deliverables, longer‑term availability gaps, and platform investments

Candidate profile

• 8+ years building, debugging, and tuning distributed and/or highly concurrent systems software (databases, storage engines, filesystems, or similar)
• 2+ years managing engineers directly, including hiring, performance management, and coaching across a range of levels
• Strong proficiency in at least one compiled, statically-typed language such as C++, Rust, or Go; prior C++ and systems programming experience is a plus
• Deep understanding of durability, availability, and performance tradeoffs in distributed systems; experience with replication, consensus, storage engines, or transactional systems is highly desirable
• Demonstrated customer obsession: direct experience working with demanding customers (or Support/Cloud) on complex incidents and turning them into clear, outcome‑oriented recommendations
• Proven ability to develop people: mentoring and promoting engineers, managing underperformance directly, and building high‑engagement teams with low regretted attrition
• Comfortable leading multi‑quarter initiatives with multiple stakeholders and using written documents to align teams on design, scope, and tradeoffs
• Able and willing to work from the New York City office most days, alongside the engineers you lead

Success measures

In three months, you have:

• Built strong relationships with your team and key partner teams
• Taken over team ceremonies, short‑term planning, and on‑call/HELP/BF responsibilities
• Co‑led at least one active project and shipped one concrete process improvement

In six to twelve months, you have:

• Led at least one cross‑team initiative in availability, durability, or developer velocity
• Delivered a multi‑quarter project with clear impact on durability/availability or team throughput
• Built a bench of engineers ready for increased scope and, over time, leadership roles

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 1273391152

We are hiring an experienced Security Software Engineer (Staff or Senior) for our Infrastructure Security team to design and build scalable security controls and services within MongoDB Atlas multi-cloud infrastructure.

The team sits within the Site Reliability Engineering organization and works with other engineering teams to ensure that our infrastructure adheres to the highest security standards.

This role can be based out of our Dublin office, or work fully remotely in Ireland.

Responsibilities:

• Design and build core security primitives and services that protect MongoDB Atlas compute, networking, and identity across AWS, Azure, and GCP
• Build secure-by-default infrastructure using Linux security mechanisms (AppArmor, SELinux, seccomp, cgroups), Kubernetes, and eBPF to enforce runtime policies and gain deep visibility into systems behaviour
• Develop APIs, automation, and tooling that manage security posture at scale (CSPM, vulnerability management, workload identity) and provide monitoring, logging, and alerting pipelines that integrate with our tooling (Grafana, Splunk, Victoria Metrics.)
• Integrate security into our CI/CD and infrastructure-as-code workflows (Terraform) so that security controls are versioned, reviewed, and deployed just like any other code
• Lead complex projects end‑to‑end, from problem discovery and design docs to implementation, rollout, and long‑term ownership
• Collaborate with SRE, platform and product engineering teams to define secure architectures for new infrastructure and services

Qualifications:

You might be a great fit if you match some of the following:

• 5+ years of experience in Software Engineering, Site Reliability Engineering, or similar roles, preferably with relevant security work
• Proficiency with at least one programming language (Java, Golang, Rust, Python, or C/C++) and experience with infrastructure-as-code tools (Terraform) to automate security configurations and processes
• A deep understanding of Linux and networking concepts, including low-level fundamentals, and how they work together in complex systems
• Experience building security-related features or platforms such as runtime monitoring, CSPM, or vulnerability management
• Experience with a cloud provider (AWS, GCP, or Azure) and their security best practices and building blocks
• Hands-on experience with Kubernetes and its security ecosystem (e.g. RBAC, OPA/Gatekeeper, Kyverno, eBPF, Falco, Tetragon)
• A systems mindset: you think about failure modes, performance, observability, and operability alongside security and correctness
• Strong communication skills and the ability to explain complex security concepts and tradeoffs to both technical and non-technical stakeholders

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

Req ID: 426183

We are hiring an experienced Security Software Engineer (Staff or Senior) for our Infrastructure Security team to design and build scalable security controls and services within MongoDB Atlas multi-cloud infrastructure.

The team sits within the Site Reliability Engineering organization and works with other engineering teams to ensure that our infrastructure adheres to the highest security standards.

This role can be based out of our New York City, Austin, Seattle or San Francisco offices, or work fully remotely on standard East Coast business hours.

Responsibilities:

• Design and build core security primitives and services that protect MongoDB Atlas compute, networking, and identity across AWS, Azure, and GCP
• Build secure-by-default infrastructure using Linux security mechanisms (AppArmor, SELinux, seccomp, cgroups), Kubernetes, and eBPF to enforce runtime policies and gain deep visibility into systems behaviour
• Develop APIs, automation, and tooling that manage security posture at scale (CSPM, vulnerability management, workload identity) and provide monitoring, logging, and alerting pipelines that integrate with our tooling (Grafana, Splunk, Victoria Metrics.)
• Integrate security into our CI/CD and infrastructure-as-code workflows (Terraform) so that security controls are versioned, reviewed, and deployed just like any other code
• Lead complex projects end‑to‑end, from problem discovery and design docs to implementation, rollout, and long‑term ownership
• Collaborate with SRE, platform and product engineering teams to define secure architectures for new infrastructure and services

Qualifications:

You might be a great fit if you match some of the following:

• 5+ years of experience in Software Engineering, Site Reliability Engineering, or similar roles, preferably with relevant security work
• Proficiency with at least one programming language (Java, Golang, Rust, Python, or C/C++) and experience with infrastructure-as-code tools (Terraform) to automate security configurations and processes
• A deep understanding of Linux and networking concepts, including low-level fundamentals, and how they work together in complex systems
• Experience building security-related features or platforms such as runtime monitoring, CSPM, or vulnerability management
• Experience with a cloud provider (AWS, GCP, or Azure) and their security best practices and building blocks
• Hands-on experience with Kubernetes and its security ecosystem (e.g. RBAC, OPA/Gatekeeper, Kyverno, eBPF, Falco, Tetragon)
• A systems mindset: you think about failure modes, performance, observability, and operability alongside security and correctness
• Strong communication skills and the ability to explain complex security concepts and tradeoffs to both technical and non-technical stakeholders

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Req ID: 2263171228

About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

What You'll Do

• Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
• Work cross-functionally with internal inference, product API, systems, and security teams, among others, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps
• Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity
• Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Are curious about LLM serving; prior inference or ML experience is not required
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Strong Candidates May Also Have Experience With

• Direct experience working with CSPs to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings
• Have experience working with external partners to align goals and deliver impact
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Solid understanding of multi-region deployments, geographic routing, and global traffic management
• Proficiency in Python or Rust

About the role

Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

Cluster Infra owns the full lifecycle of compute clusters at Anthropic. We build agent-driven automation for cluster provisioning and lifecycle management across all major cloud providers and our own datacenters. Our systems stand up clusters that are interconnected with high bandwidth, secure-by-default, and able to automatically drain and recover in response to failure. As a Staff engineer on this team, you'll set the technical direction for how Anthropic brings compute online - at a moment when the scale of that compute is growing faster than at almost any company in the world.

Key responsibilities

• Own the technical strategy and roadmap for agent-driven cluster lifecycle management - provisioning, updates and decommissioning
• Partner across teams to ensure new compute capacity is ingested on time
• Align with partner teams on physical build-out and leverage cloud solutions to deliver high-bandwidth inter-cluster connectivity
• Collaborate with security owners to ensure clusters are provisioned secure-by-default
• Define and drive strategy on cluster scalability, homogeneity and fault tolerance
• Work closely with cloud providers and internal research, inference and product teams to shape long-term compute, data, and infrastructure strategy
• Establish and evolve operational-excellence practices: incident response, postmortem culture and on-call health
• Support the growth of engineers around you through technical mentorship and coaching

Minimum qualifications

• Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)
• Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.
• Track record of leading complex, multi-quarter technical initiatives spanning multiple teams or systems
• Ability to build alignment across senior stakeholders and communicate effectively at all levels

Preferred qualifications

• 8+ years of software engineering experience, including time as a technical lead setting direction for a team
• Experience operating large-scale compute infrastructure at hyperscale (100+ clusters, 10K+ nodes)
• Depth in one or more of: Kubernetes internals, cluster provisioning and management systems, cluster orchestration systems (Mesos, Borg-like)
• Experience with cloud networking: VPC design and peering, Shared VPC/Transit Gateway, Cloud Interconnect/Direct Connect, Cloud NAT, cross-cloud private connectivity, BGP and route control, edge load balancing and DDoS mitigation (Cloud Armor / AWS Shield)
• Experience with cluster and host networking: CNI (Cilium), eBPF, NetworkPolicy, multi-NIC, sFlow, service mesh (Istio/Envoy/Linkerd, mTLS)
• Experience with cluster security: pod security standards and admission control, RBAC and least-privilege IAM, node and container hardening, supply-chain/image provenance
• Deep experience with infrastructure-as-code (Terraform, Atlantis), workflow orchestration (Temporal, Argo Workflows)
• Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems

About the team

Join our team as a Platform Engineer focused on network architecture and site reliability. In this role, you will own the design and implementation of our cloud network architecture, ensuring our platform is resilient, secure, and scales effectively across multiple AWS accounts, environments, and regions. You'll architect solutions for complex networking challenges like hierarchical CIDR allocation across accounts and integrations, implement disaster recovery and regional redundancy strategies, and establish the reliability practices that keep our healthcare platform running. Working closely with leadership and cross-functional teams, you'll build the foundational infrastructure that supports our rapidly growing platform while ensuring we can handle failures gracefully and recover quickly.

Position Responsibilities:

In this role, you'll own critical infrastructure that ensures platform reliability and scalability. You will:

• Design network architecture: Architect and implement hierarchical CIDR allocation strategies across multiple AWS accounts, environments, and external integrations, ensuring proper subnet organization and IP address management.
• Build multi-region capabilities: Design and implement regional redundancy and multi-region failover strategies to support disaster recovery requirements and improve platform availability.
• Manage cloud networking: Own VPC design, subnets, routing tables, security groups, NACLs, VPC peering, Transit Gateway, and other AWS networking components across our infrastructure.
• Implement disaster recovery: Establish and regularly test disaster recovery procedures, backup strategies, and failover mechanisms to ensure business continuity.
• Evaluate multi-cloud solutions: Assess and prototype solutions across AWS, Azure, GCP, and Oracle Cloud to determine optimal approaches for specific use cases and integrations.
• Monitor platform health: Define and implement platform health indicators, SLIs, SLOs, and monitoring that provide early warning of infrastructure issues and track reliability improvements.
• Ensure security and compliance: Work with security teams to implement network security best practices, maintain compliance requirements (HIPAA, SOC2, FedRAMP), and conduct security reviews.
• Drive infrastructure reliability: Participate in on-call rotations, lead incident response for infrastructure issues, conduct postmortems, and implement improvements to prevent recurrence.
• Automate infrastructure management: Build infrastructure-as-code using Terraform to manage network resources, implement self-service capabilities, and ensure consistency across environments.

Requirements:

• 5+ years of infrastructure engineering, DevOps, or site reliability engineering experience.
• Experience leading projects or teams: Demonstrated ability to lead technical initiatives, drive architectural decisions, or mentor other engineers.
• Extensive AWS expertise: Deep production experience with VPC, subnets, routing, security groups, Transit Gateway, Direct Connect, Route 53, and other AWS networking services.
• Network architecture experience: Proven track record designing and implementing complex network architectures including CIDR planning, subnet design, and multi-account strategies.
• Disaster recovery expertise: Experience designing and testing disaster recovery procedures, backup strategies, and multi-region failover capabilities.
• Infrastructure as code: Strong experience with Terraform for managing cloud infrastructure at scale.
• Site reliability practices: Understanding of SLIs, SLOs, error budgets, incident management, and reliability engineering principles.
• Strong development skills: Proficiency in Python or similar languages for automation and tooling.
• Security mindset: Understanding of network security principles, encryption, IAM, and compliance requirements in regulated industries.

Preferred Qualifications: 

• Multi-cloud experience: Hands-on experience with Azure, GCP, or Oracle Cloud networking and cross-cloud connectivity.
• Rust development experience or interest in learning Rust for infrastructure tooling.
• Advanced AWS certifications: AWS Solutions Architect Professional, AWS Advanced Networking, or similar certifications.
• Container networking: Experience with ECS, EKS, or Kubernetes networking, service mesh, and load balancing.
• Database networking: Understanding of database replication, Aurora Global Databases, and cross-region data synchronization.
• CDN and edge networking: Experience with CloudFront, edge locations, and content delivery optimization.
• Cost optimization: Track record of reducing infrastructure costs while maintaining or improving reliability.
• Previous Pharmacy Benefits Manager (PBM) or healthcare technology experience.

Role Summary:

Datadog is seeking a Staff Software Engineer to help shape the future of our Bring Your Own Cloud (BYOC) Logs offering by unifying observability pipelines with log management software that customers deploy and manage in their own infrastructure. This role will focus on building and scaling systems that process, route, and store high-volume observability data within customer-managed infrastructure. You will operate as a hands-on technical leader, driving architecture, cross-team delivery, and product direction across a complex and evolving space. This is a high-impact opportunity to influence product strategy, mentor engineers, and solve deeply technical challenges at scale.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Make customer-controlled deployments feel like a managed Datadog product: deployment, upgrades, configuration, observability, diagnostics, reliability, and secure operation across diverse customer cloud environments
• Build and scale high-throughput systems for log processing, routing, and transformation across distributed environments
• Lead cross-team initiatives, aligning engineers, product managers, and stakeholders to deliver complex, multi-team projects
• Design and implement software that runs reliably that customers deploy and operate within their own cloud infrastructure.
• Improve system performance, scalability, and cost efficiency through thoughtful trade-off analysis and capacity planning
• Contribute hands-on to critical code paths, debugging, and deployment challenges in customer environments
  
  

Who You Are:

• You have significant experience building software that is installed, deployed, and operated in customer environments rather than only as a fully managed SaaS service.
• You have strong expertise in distributed systems, including scalability, performance optimization, and high-throughput data processing
• You are proficient in systems-level programming (e.g., Go, Rust, or C++) and understand how software runs across diverse environments (e.g., Linux systems)
• You have experience with cloud platforms (e.g., AWS, Azure, or GCP) and are comfortable troubleshooting infrastructure and networking issues
• You are experienced with containerization and orchestration technologies such as Kubernetes in production environments
• You have a track record of leading large, cross-functional engineering efforts and influencing technical direction across teams
  
  

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Generous and competitive benefits package
• New hire stock equity (RSUs) and employee stock purchase plan
• Continuous career development and pathing opportunities
• Employee-focused best in class onboarding
• Internal mentor and cross-departmental buddy program
• Friendly and inclusive workplace culture

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

Location: New York City | Team: Trading Technology | Type: Full-Time

As a market leader serving hedge funds, asset managers, and financial institutions, we are building a next-generation platform for high- and low-touch trading workflows across spot, derivatives, and structured products.

The Role

We’re looking for a Senior Trading Systems Developer with strong experience in low-latency Java and/or Rust to architect and build our next-generation trading applications and execution platform. This platform will be central to supporting complex electronic trading algos, such as automated market making and quant trading strategies.  You will work on the core components that sit on the hot path of our execution stack — including market-data ingestion, order management, risk checks, session control, and high-throughput venue connectivity. 

You’ll be designing and developing scalable, low-latency components that are resilient under heavy load and tightly integrated with our execution stack and internal trading infrastructure.

What You’ll Do

• Responsible for development of low latency services on Aeron, including OMS/EMS components, market-data pipelines, and venue gateway connectivity.
• Integrate with exchange APIs using FIX, REST, WebSocket, and venue-specific protocols.
• Model complex order lifecycles, state machines, and execution workflows into resilient, observable, fault-tolerant services.
• Design and maintain SBE message schemas and high-throughput message-bus communication on Aeron.
• Own full SDLC for your domain: design, test, ship, monitor
• Collaborate closely with quant, trading, infra, and data teams to ensure the execution stack meets performance, safety, and reliability targets.
• Operate in a fast-paced Agile environment, contributing to team structure, sprint planning, and execution cadence
  
  

What We’re Looking For

• 5+ years of experience in trading systems, ideally with prior design ownership
• Strong background in Java (11+) and/or Rust, including performance profiling, concurrency, lock-free programming, GC tuning, or memory-safe systems design.
• Experience in building EMS and execution systems that operate in microseconds latency rang
• Deep understanding of order state machines, execution workflows, and institutional trade lifecycle mechanics
• Strong experience with Aeron or other high-throughput messaging frameworks.
• Hands-on experience integrating with FIX, REST, and WebSocket APIs.
• Strong experience designing normalized schemas and working with relational databases (e.g., Postgres, MySQL)
• Experience with low-touch and high-touch trading workflows (MM, RFQ, internalization).
• Experience with middle and back office, such as booking, settlement and clearing
• Experience with Agile delivery, sprint planning, and team leadership
• Passion for clean architecture, maintainability, and performance
  
  

Bonus Points

• Prior experience in crypto markets or electronic trading in traditional finance.
• Experience integrating execution platforms with strategy containers, risk engines, or smart order routers.
• Knowledge of FIX protocol nuances and connectivity to major exchanges.
• Exposure to container orchestration, service mesh, or hybrid deployment architectures.

The base pay for this role is expected to be between $179,000 and $226,000 in New York City. This expected base pay range is based on information at the time this post was generated. This role will also be eligible for other forms of compensation such as a performance linked bonus, equity, and a competitive benefits package. Actual compensation for a successful candidate will be determined based on a number of factors such as location, skillset, experience, qualifications and the level at which the candidate is hired.

• Official Communication: We only communicate through official email addresses ending in @falconx.io. If you receive an email from a public domain (Gmail, Yahoo, etc.) or a lookalike domain, it is not from us.
• Official Channels: We will never conduct interviews via Telegram, WhatsApp, or Signal. Our process involves video or in-person meetings.
• No Financial Requests: FalconX will never ask you for money, "equipment fees," "training deposits," or crypto transfers at any stage of the recruitment process.
• Verification: All legitimate FalconX openings are listed on our official careers site: [Link to falconx.io/careers].

About Us

AXQ Capital is a global quantitative investment firm with offices in New York, Beijing, Shanghai, and Hong Kong. We pursue consistent alpha through rigorous scientific research and sustained investment in technology and data infrastructure. Our strategies are deployed across global markets, spanning multiple geographies, asset classes, and trading horizons.

Responsibilities 

As a Quantitative Developer, you will work closely with our research and engineering teams to contribute to solutions across strategy research, systematic trading, and risk management. Working under the guidance of senior team members, your work will include:

• Contributing to global multi-market connectivity and cross-region, multi-asset-class data, research, backtesting, and trading systems — taking ownership of well-scoped components and growing into broader areas of the stack

• Helping build and maintain event-driven backtesting and simulation frameworks, with attention to consistency in data semantics and time models across research, backtesting, and production environments

• Implementing and improving pieces of our order management, execution, and real-time risk systems, with a focus on writing correct, well-tested, and performant code

Requirements

• 0–3 years of software engineering experience, including internships; prior exposure to quantitative finance is a plus but not required

• Bachelor's degree or higher in Computer Science, Natural Sciences, Engineering, Financial Mathematics, or a related quantitative field

• Solid programming skills and computer science fundamentals; proficient in Python, with working knowledge of (or strong interest in learning) a statically-typed language such as C++, Rust, or Go

• Familiarity with Linux, basic networking, and concurrency concepts; willingness to deepen these skills on the job

• Curiosity, drive, and the ability to learn quickly when tackling unfamiliar problems

• Sense of ownership over the work you take on, and willingness to ask questions early

• Internship, research, or personal project experience related to trading systems, market data, or backtesting

• Coursework, competitions, or open-source contributions demonstrating strong engineering or quantitative skills (e.g., ICPC, Kaggle, well-maintained GitHub projects)

• Effective use of AI-assisted coding tools (e.g., Claude Code, Cursor, Codex)

About the role

Anthropic runs some of the largest Kubernetes clusters in the industry. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. The Kubernetes Platform team owns the Kubernetes control plane that makes those clusters work.

We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself — apiserver, etcd, controllers — so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure.

We make sure the control plane is fast, correct, and always available. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow.

Key responsibilities

• Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption
• Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us
• Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on
• Build and maintain custom controllers, operators, and CRDs
• Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities
• Collaborate with cloud providers on required features and escalations
• Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures

Minimum qualifications

• Significant software engineering experience building and operating production distributed systems
• Proficiency in at least one systems-appropriate language (e.g., Go, Python, Rust, or C++)
• Deep, hands-on Kubernetes experience (well beyond "user of”) into scheduler, controllers, apiserver, or operating large multi-tenant clusters
• Demonstrated ability to debug complex issues across the stack, from API behavior down to node and network-level root causes
• A track record of designing for reliability, correctness, and clear failure semantics in systems other engineers depend on
• Strong written and verbal communication; comfort building consensus with internal stakeholders

Preferred qualifications

• Experience with Kubernetes internals or contributions: kube-scheduler / scheduling framework, apiserver, etcd, client-go, controller-runtime, or similar
• Experience building or operating cluster schedulers or batch systems (e.g., Kueue, Volcano, Slurm, or in-house equivalents)
• Background scaling control planes or coordination systems (etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments)
• Familiarity with ML infrastructure: GPUs, TPUs, or Trainium; gang scheduling; topology-aware placement; collective networking such as NCCL
• Experience with GCP and/or AWS, including GKE/EKS internals and Infrastructure as Code
• Low-level systems experience such as Linux kernel tuning, cgroups, or eBPF
• 8+ years of relevant industry experience, including time leading large, ambiguous infrastructure projects

About the Company

Gemini is a global crypto and Web3 platform founded by Cameron and Tyler Winklevoss in 2014, offering a wide range of simple, reliable, and secure crypto products and services to individuals and institutions in over 70 countries. Our mission is to unlock the next era of financial, creative, and personal freedom by providing trusted access to the decentralized future. We envision a world where crypto reshapes the global financial system, internet, and money to create greater choice, independence, and opportunity for all — bridging traditional finance with the emerging cryptoeconomy in a way that is more open, fair, and secure. As a publicly traded company, Gemini is poised to accelerate this vision with greater scale, reach, and impact.

The Department: Customer Support (Ledger Operations)

As a team within the Support group, the team is data driven and customer-centric. Team members work closely with data scientists, engineers, product managers, and corporate operational stakeholders to reconcile data. Projects range from the very urgent, short sprint to redesigning long-term solutions to improve scalability and automation. Ledger Operations primary goal is to ensure high quality, scalable data reconciliation with proactive monitoring and reducing delays for reactive corrections. This team is responsible for building the “next generation” of reconciliation tools to maintain and expand Gemini’s internal reconciliation processes. 

The Role: Senior Ledger Operations Engineer

This role is required to be in person twice a week at our New York City, NY office.

Responsibilities:

• Build and maintain API adapters for Gemini trade data
• Design and optimize data pipelines for ledger operations
• Automate ETL (Extract, Transform, Load) processes
• Optimize SQL queries and database performance
• Contribute to the next generation of ledger reconciliation processes and reporting
• Collaborate with third-party vendors, Data Analytics team, and Cryptocore engineers
• Support data processing for the blockchain reconciliation project
• Develop and maintain data adapters for real-time data processing
• Create and implement data validation processes
• Develop customized reporting solutions
• Investigate and identify root causes of exception reports

Minimum Qualifications:

• 5+years implementing a reconciliation system while improving existing data pipelines
• Experience building real-time data solutions, data-lake and data warehousing technologies
• Experience building and integrating web analytics solutions
• Advanced SQL skills is a must
• Experience with gRPC, Websocket and REST API 
• Experience and expertise in Databricks, Spark/PySpark, Kafka streaming, Apache Flink, Airflow, Hadoop etc.
• Skilled in programming languages Python, Scala, Rust or Go
• Experience with one or more MPP databases(Redshift, Timescale,  StarRock)
• Able to identify gaps in existing reconciliation processes
• Extensive ETL and database experience with financial transaction systems for (banking systems, exchange systems)
• Experience with Datadog, cloudwatch or other alert monitoring systems

Preferred Qualifications:

• Financial certifications are not required, but encouraged. Understanding the regulatory landscape in traditional financial markets will lend well as the digital asset space evolves
• Financial Reporting Requirements for publicly traded companies

• Competitive starting pay
• A discretionary annual bonus
• Long-term incentive in the form of a new hire equity grant
• Comprehensive health plans
• 401K with company matching
• Paid Parental Leave
• Flexible time off

Salary Range: The base salary range for this role is between $126,000 - $180,000 in the State of New York, the State of California and the State of Washington. This range is not inclusive of our discretionary bonus or equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.

In the United States, we offer a hybrid work approach at our hub offices, balancing the benefits of in-person collaboration with the flexibility of remote work. Expectations may vary by location and role, so candidates are encouraged to connect with their recruiter to learn more about the specific policy for the role. Employees who do not live near one of our hubs are part of our remote workforce.

At Gemini, we strive to build diverse teams that reflect the people we want to empower through our products, and we are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Equal Opportunity is the Law, and Gemini is proud to be an equal opportunity workplace. If you have a specific need that requires accommodation, please let a member of the People Team know.

#LI-AA1

About the Role

Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

Developer Productivity owns the end-to-end experience of how engineers and researchers at Anthropic develop, build, test, and ship code at scale — from the source control and language ecosystems that underpin our monorepo, to the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers, to the developer acceleration tooling that deeply integrates Claude into engineering workflows.

Team Matching: Team matching is determined after the interview process based on interview performance, interests, and business priorities. Please note we may also consider you for different Infrastructure teams. 

Responsibilities:

• Own the technical strategy and roadmap for your area, translating team-level goals into concrete execution plans
• Define infrastructure architecture, ensuring the hardest problems get solved — whether by you directly or by working through others
• Design and build scalable, reliable distributed infrastructure and shared libraries that support high-volume workloads across all engineering teams
• Own and evolve build environments, package management, and dependency systems to enable fast, reproducible builds
• Define and implement language ecosystem standards, tooling, and frameworks that drive developer productivity across research and production workloads

You may be a good fit if you:

• Have deep experience with build systems, CI/CD pipelines, and/or developer tooling in a large monorepo environment
• Have strong proficiency in Python, Rust and/or Go
• Are obsessed with developer productivity and reducing friction in the software development lifecycle
• Have experience with container orchestration and infrastructure at scale
• Have excellent communication skills and enjoy supporting internal partners to improve their development experience
• Are excited about designing foundational systems and are comfortable working independently on ambiguous, high-impact technical challenges

Strong candidates may have:

• 15+ years (not including internships or co-ops) of experience in a Software Engineer role, building and operating large-scale developer infrastructure
• 3+ years (not including internships or co-ops) of experience leading large scale complex projects or teams as a tech lead
• Experience with CI orchestration tools (Buildkite, Jenkins, GitHub Actions, or similar) and merge queue management at scale
• Experience building or operating remote build execution systems (Bazel Remote Execution API, BuildBarn, BuildBuddy, or similar)
• Experience with Nix/NixOS/Docker and managing large image / package sets at scale
• Experience building CLI tools, developer-facing services, and GitHub API and automation workflows

Deadline to apply: None. Applications will be reviewed on a rolling basis.

What You’ll Do:

The Storage Engine Organization at CoreWeave is responsible for the product capabilities and data plane function of CoreWeave’s managed storage products. We build reliable, scalable storage solutions with segment leading performance. Storage engine works with engineering teams across infrastructure, compute, and platform to ensure our storage services meet the needs of the world’s most demanding AI workloads.

About the role:

• Design and Implement distributed storage solutions to support scaling data intensive AI workloads.
• Contribute to the development of exabyte-scale, S3-compatible object storage and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency.
• Participate in efforts to improve the reliability, durability, and observability of our storage stack.
• Collaborate with operations teams to monitor, troubleshoot, and improve storage systems in production environments.
• Help develop metrics and dashboards to provide visibility into storage performance and health.
• Build proactive automation to ensure our systems have a consistent performance envelope for customer workloads.
• Analyze telemetry and system data to drive improvements in throughput, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4–8 years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C, or Rust.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architecture.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love to grow and push the boundaries of your expertise
• You’re curious about distributed storage and the demands AI/ML workloads
• You’re an expert in data persistence on physical media, high performance data transfer using RDMA, or resilient distributed systems.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Storage Engine Team at CoreWeave is responsible for the product capabilities and data plane function of CoreWeave’s managed storage products. We build reliable, scalable storage solutions with segment leading performance. Storage engine works with engineering teams across infrastructure, compute, and platform to ensure our storage services meet the needs of the world’s most demanding AI workloads.

About the role:

• Design and Implement distributed storage solutions to support scaling data intensive AI workloads.
• Contribute to the development of exabyte-scale, S3-compatible object storage and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency.
• Lead efforts to improve the reliability, durability, security, and observability of our storage stack.
• Collaborate with operations teams to monitor, troubleshoot, and improve storage systems in production environments.
• Set the bar for developing metrics and dashboards to provide visibility into storage performance and health.
• Analyze telemetry and system data to drive improvements in throughput, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are: (This section has to be the technical and measurable requirement of the role, soft skills cannot be measured) 

• Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, or a related field.
• 8–10+ years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C, or Rust.
• Proficiency leveraging AI tools to augment software development.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love to grow and push the boundaries of your expertise
• You’re curious about distributed storage and the demands AI/ML workloads
• You’re an expert in data persistence on physical media, high performance data transfer using RDMA, or resilient distributed systems.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Storage Engine Team at CoreWeave is responsible for the product capabilities and data plane function of CoreWeave’s managed storage products. We build reliable, scalable storage solutions with segment leading performance. Storage engine works with engineering teams across infrastructure, compute, and platform to ensure our storage services meet the needs of the world’s most demanding AI workloads.

About the role:

• Design and Implement distributed storage solutions to support scaling data intensive AI workloads.
• Contribute to the development of exabyte-scale, S3-compatible object storage and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency.
• Lead efforts to improve the reliability, durability, security, and observability of our storage stack.
• Collaborate with operations teams to monitor, troubleshoot, and improve storage systems in production environments.
• Set the bar for developing metrics and dashboards to provide visibility into storage performance and health.
• Analyze telemetry and system data to drive improvements in throughput, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are: 

• Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, or a related field.
• 8–10+ years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C, or Rust.
• Proficiency leveraging AI tools to augment software development.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

We’re looking for a Staff Storage Engineer to play a key role in designing, building, and operating the control plane for our high-performance AI storage platform. You’ll help evolve CoreWeave’s storage systems by building reliable, scalable, and high-throughput solutions that power some of the largest and innovative AI workloads in the world. This role involves close collaboration with teams across infrastructure, compute, and platform to ensure our storage services scale automatically and seamlessly while maximizing performance and reliability.

About the role: 

• Design and implement a highly scalable multi-tenant control plane that supports CoreWeave’s growing AI storage and cloud infrastructure needs.
• Contribute to the development of exabyte-scale, S3-compatible object storage, distributed file system and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency.
• Participate in efforts to improve the reliability, durability, and observability of our storage stack.
• Collaborate with operations teams to monitor, analyze, and optimize storage systems using telemetry, metrics, and dashboards to improve performance, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are:  

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 10+ years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C++, or Rust.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Solid understanding of cloud-native infrastructure, Kubernetes, and scalable system architecture.
• Strong debugging and problem-solving skills in distributed, high-performance environments.
• Clear communicator, able to work collaboratively across teams and share technical insights effectively.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Storage Engine Team at CoreWeave is responsible for the product capabilities and data plane function of CoreWeave’s managed storage products. We build reliable, scalable storage solutions with segment leading performance. Storage engine works with engineering teams across infrastructure, compute, and platform to ensure our storage services meet the needs of the world’s most demanding AI workloads.

About the role:

• Design and Implement distributed storage solutions to support scaling data intensive AI workloads.
• Contribute to the development of exabyte-scale, S3-compatible object storage and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency.
• Lead efforts to improve the reliability, durability, security, and observability of our storage stack.
• Collaborate with operations teams to monitor, troubleshoot, and improve storage systems in production environments.
• Set the bar for developing metrics and dashboards to provide visibility into storage performance and health.
• Analyze telemetry and system data to drive improvements in throughput, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are: 

• Bachelor’s, Master’s, or PhD degree in Computer Science, Engineering, or a related field.
• 8–10+ years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C, or Rust.
• Proficiency leveraging AI tools to augment software development.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love to grow and push the boundaries of your expertise
• You’re curious about distributed storage and the demands AI/ML workloads
• You’re an expert in data persistence on physical media, high performance data transfer using RDMA, or resilient distributed systems.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! 

The base salary range for this role is $143,000 to $210,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

The Runtime & GPU Systems team builds and operates secure, high-performance environments for multi-tenant Kubernetes platforms at scale. Our team sits at the intersection of container runtimes, lightweight virtualization, GPU infrastructure, and Linux systems engineering. We partner closely with security, platform, and GPU infrastructure teams to deliver robust, isolated, and efficient execution environments for AI-driven workloads.

About the role:

As a GPU & Runtime Systems Engineer, you will design, build, and evolve secure sandboxed runtime environments for Kubernetes workloads, focusing on runtime isolation, performance, and security. You will integrate container runtimes, lightweight VMs, and virtualization technologies to support GPU-accelerated workloads in multi-tenant environments. Day-to-day, you’ll develop GPU-aware sandboxing and scheduling strategies, optimize containerization and I/O performance for latency-sensitive workloads, and influence architectural decisions across Linux internals, container runtimes, virtualization layers, and GPU drivers.

Some of what you’ll work on:

• Design and implement secure execution environments for containerized and virtualized workloads.
• Build GPU-aware scheduling, isolation, and resource management strategies for multi-tenant workloads.
• Optimize container, VM, and I/O performance across GPU-accelerated workloads.
• Conduct profiling, benchmarking, and performance tuning for runtime, virtualization, and GPU stacks.
• Contribute to architectural decisions across Linux internals, container runtimes, virtualization layers, and GPU drivers.
• Collaborate with security, platform, and infrastructure teams to define and implement runtime isolation and performance standards.

Who You Are:

• 3+ years of experience in systems, platform, infrastructure, or production engineering at scale.
• Strong hands-on experience with Kubernetes, container orchestration, and cloud-native architectures, including controllers, operators, or scheduling extensions.
• Experience designing, implementing, or operating secure execution environments (container runtimes, sandboxed workloads, or virtualized systems).
• Practical experience with lightweight virtualization and sandboxing technologies (e.g., Kata Containers, gVisor, KubeVirt, QEMU).
• Experience supporting GPU-accelerated workloads in multi-tenant environments, including GPU scheduling, isolation, device passthrough, mediated devices, or virtualization.
• Proficient in systems-oriented programming (Go, C/C++, Rust, Bash) with strong Linux internals knowledge.
• Skilled at diagnosing and resolving complex performance, reliability, or isolation issues across containers, VMs, and infrastructure.
• Experienced in profiling, benchmarking, and tuning performance across runtime, virtualization, and GPU stacks.

Preferred:

• Experience building systems for safely executing untrusted or sensitive workloads in shared environments.
• Familiarity with GPU drivers and low-level virtualization or I/O optimization techniques.
• Experience defining threat models and implementing runtime security policies in multi-tenant systems.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love building high-performance systems that operate reliably under extreme scale and demand.
• You’re curious about the intersection of security, virtualization, Kubernetes, and GPU infrastructure.
• You’re an expert in reasoning about trade-offs between isolation, performance, and operability.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $139,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

We’re looking for a Senior Storage Engineer to play a key role in designing, building, and operating the control plane for our high-performance AI storage platform. You’ll help evolve CoreWeave’s storage systems by building reliable, scalable, and high-throughput solutions that power some of the largest and innovative AI workloads in the world. This role involves close collaboration with teams across infrastructure, compute, and platform to ensure our storage services scale automatically and seamlessly while maximizing performance and reliability.

About the role:

• Design and implement a highly scalable multi-tenant control plane that supports CoreWeave’s growing AI storage and cloud infrastructure needs.
• Contribute to the development of exabyte-scale, S3-compatible object storage, distributed file system and integrate dedicated storage clusters into diverse customer environments.
• Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency.
• Participate in efforts to improve the reliability, durability, and observability of our storage stack.
• Collaborate with operations teams to monitor, analyze, and optimize storage systems using telemetry, metrics, and dashboards to improve performance, latency, and resilience.
• Work cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.
• Share your knowledge and mentor other engineers on best practices in building distributed, high-performance systems.

Who You Are: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6–10 years of experience working in storage systems engineering or infrastructure.
• Strong hands-on experience with object storage or distributed filesystems in production environments.
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar.
• Proficiency in a systems programming language such as Go, C, or Rust.
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana).
• Solid understanding of cloud-native infrastructure, Kubernetes, and scalable system architecture.
• Strong debugging and problem-solving skills in distributed, high-performance environments.
• Clear communicator, able to work collaboratively across teams and share technical insights effectively.

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do:

We are looking for a highly experienced and strategic Principal Engineer, Observability to lead the architecture, development, and operations of our Observability platform. In this role, you will define how customers monitor, troubleshoot, and operate their AI workloads at scale on CoreWeave.

You will work directly with customers and partner closely with engineering leaders across multiple teams to drive a unified Observability experience across CoreWeave products—spanning metrics, logs, traces, and customer-facing insights.

About the role:

• Lead the Observability strategy and roadmap, ensuring clear alignment with business goals, product direction, and performance/SLA objectives
• Design and implement low-latency, high-scale telemetry pipelines and data stores solutions that power observability across all CoreWeave products
• Build customer-facing experiences—dashboards, alerts, and workflows—that enable rapid troubleshooting and deep insight into AI workloads and platform health
• Drive reliability, durability, and self-healing across the Observability stack, owning key services in production and setting a high bar for operational excellence
• Shape customer experience for visibility by defining standards for metrics, SLOs, and dashboards that expose system performance and reliability in a clear, actionable way
• Analyze production telemetry to identify and prioritize improvements in performance, reliability, and efficiency across the platform
• Act as a trusted advisor to senior leadership, bringing perspective on observability trends and advocating for the right long-term technology investments
• Partner with engineering, infrastructure, and product teams to ensure Observability is deeply integrated into evolving architectures, APIs, and customer features
• Mentor and uplevel engineers across teams, promoting best practices in observability, distributed systems, and customer-focused product engineering

Who You Are:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
• 15+ years of experience building and operating distributed systems at scale, with a strong track record of reliability-focused engineering
• Proven experience leading Observability platform engineering initiatives and delivering products that directly address customer needs
• Strong proficiency in one or more programming languages such as Go, Python, or Rust, and comfort working in large-scale production environments
• Deep understanding of distributed observability systems and telemetry pipelines (e.g., ClickHouse or similar technologies for telemetry at scale)
• Strong understanding of cloud-native infrastructure—Kubernetes, scalable architectures, automation, and modern CI/CD and deployment practices
• Excellent analytical and problem-solving skills, including the ability to synthesize ambiguous customer and system signals into clear problem statements and designs
• Exceptional communication and collaboration skills, able to articulate observability strategies, trade-offs, and best practices to both technical and non-technical stakeholders
• Prior experience building end-to-end Observability solutions (metrics, logs, traces, dashboards, alerting) in production environments

Preferred:

• Experience with modern AI platforms and workloads (e.g., large-scale training/ inference, GPU-based infrastructure, MLOps tooling) is a plus

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $206,000 to $303,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What You’ll Do:

The Platform & Infrastructure Engineering team in the Data Infrastructure organization is responsible for the performance, reliability, scalability, and security of the company’s data platform. The team builds and operates the foundational systems that power ingestion, transformation, analytics, and AI workloads at scale. This includes ownership of the underlying infrastructure for orchestration, compute, and storage systems that enable data engineering teams to build and deliver data products. We operate with production-grade discipline, supporting mission-critical services with stringent uptime requirements and a focus on automation, observability, and resilience.

About the role:

As an Engineering Manager, you will lead a team of Software Engineers and Site Reliability Engineers responsible for the infrastructure that powers CoreWeave’s data platform. You will own the reliability, scalability, and performance of core systems such as compute engines, orchestration frameworks, and storage layers. You’ll partner closely with Data Engineering teams, as well as cross-functional groups including Production Engineering, Developer Experience, Security Engineering, and IT Operations to ensure the platform is robust, secure, and easy to operate. This role balances people leadership with deep technical ownership, including stepping in hands-on when needed to support critical initiatives.

Who You Are: 

• 7+ years of experience in software engineering, infrastructure engineering, or data platform engineering roles
• 2+ years of experience managing engineering teams, including hiring, coaching, performance management, and career development
• Experience leading teams through the full software development lifecycle (SDLC), including planning, execution, and delivery of complex technical initiatives
• Experience running and evolving engineering processes (e.g., agile development, backlog management) to drive predictable execution and continuous improvement
• Experience setting team goals and metrics (e.g., OKRs) and holding teams accountable to outcomes
• Strong hands-on experience operating and scaling data platform infrastructure (e.g., Spark, Airflow, Iceberg, StarRocks) in production environments
• Deep expertise in Kubernetes and containerized software development, including cluster design, operations, and scaling in production environments
• Experience building and operating distributed systems with high availability and performance requirements, including SLOs and incident management
• Strong understanding of data platform architecture (compute, orchestration, storage) and experience driving reliability, performance, and cost optimization at the platform level
• Ability to contribute code and technical solutions when needed, with proficiency in at least one programming language (Python, Java, Go, Rust)
• Experience partnering with cross-functional engineering teams (e.g., Production Engineering, Developer Experience, Security, IT) and data engineering teams to deliver cohesive platform solutions

Preferred: 

• Experience supporting high-scale data workloads (e.g., large-scale Spark clusters, real-time ingestion platforms)
• Experience working in environments with strict uptime and reliability requirements (e.g., ≥99.99% uptime)
• Experience working in regulated environments with compliance frameworks such as GDPR, SOC 2, HIPAA, or SOX
• Experience building internal platforms that enable self-service analytics or developer productivity 

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love building and leading teams that operate critical infrastructure at scale
• You’re curious about how to improve reliability, performance, and cost efficiency across complex data systems
• You’re an expert in navigating tradeoffs across platform architecture, system design, and engineering execution

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

The base salary range for this role is $165,000 to $242,000 The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

As a Staff Application Security Engineer at Datadog, you'll set technical direction for how we approach application security at scale. You'll define the frameworks, methodologies, and architectural patterns that engineering teams across Datadog adopt and apply independently. You're the person others come to when they don't know how to make something secure, and you reliably have an answer.

You'll be a point of contact for our most complex security programs, often spanning multiple teams and multiple quarters. The role requires both depth (going very deep on specific problems when needed) and breadth (recognizing patterns across systems and drawing connections that others miss). Partnering closely with teams inside and outside the security org is key to success. You'll help shape the AppSec roadmap and make the case for where investment should go. 

We use our own platform. Logs, Dashboards, Service Catalog, and APM aren't just things we sell: they're tools the AppSec team uses to build security services, measure adoption of secure defaults, and communicate risk across the organization.

AI is also part of the picture. Engineering at Datadog increasingly uses agentic tooling throughout the development lifecycle, and many of the products we ship to customers now include AI-powered features. Both create new attack surfaces, and defining our strategy for addressing them is part of this role.

If using Datadog to observe Datadog's own security posture, building impactful tooling, and shaping how we secure AI-powered systems sounds like the right kind of problem, this role is worth a close look.

What You’ll Do:

• Define and drive security standards and secure-by-default solutions, serving as the Application Security subject matter expert.

• Build security tooling and automation that scales security practices across engineering teams, and implement robust security observability to support our threat detection team with meaningful, actionable security signals.

• Lead threat modeling and risk assessment for high-risk features and platform changes.

• Assess and address security risks introduced by agentic development practices and AI-powered product features in production

• Partner with engineering teams to prioritize and remediate critical threats, define API security standards, and conduct security code reviews.

• Identify systemic security risks; lead complex, multi-team remediation efforts end-to-end

• Partner with Cloud & Infrastructure Security and other teams across the org on cross-domain problems; be the AppSec point of contact on complex cross-domain problems

• Serve as the AppSec subject matter expert across Datadog; be the person engineering leadership calls when they need clarity on a hard security problem

• Deeply invest in the growth of AppSec engineers on the team

Who You Are: 

• Software engineering background with hands-on code review experience; Go (preferred), Python, or Rust

• Demonstrated ability to level up the engineers around you: through design reviews, mentorship, and the quality of your documentation

• Solid grounding in OWASP Top 10, web vulnerabilities (XSS, injection, access control, cryptography), SAST, and DAST

• Working knowledge of API security: authentication flows, authorization patterns, and input validation at API boundaries

• Track record of leading threat modeling on complex, multi-team systems and translating outcomes into architectural decisions

• Experience implementing secure-by-default frameworks and integrating security into core platforms alongside product managers and engineering teams

• Able to translate business risk into security investment priorities and communicate tradeoffs clearly to executive audiences

• Familiarity with software supply chain security: dependency management, artifact integrity, and build pipeline trust

• Bias toward implementing solutions and driving adoption, not just surfacing findings

• Proven track record of winning buy-in from technical and non-technical stakeholders; able to communicate complex tradeoffs clearly to engineers, product managers, and leadership

• Current on security best practices, emerging threats, and the tooling landscape

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)

• Continuous professional development, product training, and career pathing

• Intradepartmental mentor and buddy program for in-house networking

• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

• Access to Inclusion Talks, our internal panel discussions

• Free, global mental health benefits for employees and dependents age 6+

• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

Distributed Systems engineers at Datadog design, implement and run in production the foundational platforms powering our applications. Your data pipelines will ingest, store, analyze and query in real-time billions of events per second from companies all over the globe. The platforms are optimized for durability, high availability, low latency, internet-scale footprint and operability. 

At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What You’ll Do:

• Build fault-tolerant, horizontally scalable solutions running in multi-tenant environments
• Write in Go, Java Rust or C++, amongst other languages
• Use Kafka, Redis, Cassandra, Elasticsearch and other open-source components
• Own meaningful parts of our service, have an impact, grow with the company
  
  

Who You Are:

• 6+ years of experience
• You have a BS/MS/PhD in a scientific field or equivalent experience
• You have significant backend programming experience in one or more languages (Go, Java, Rust, C++) 
• You have been exposed to working on problems (high durability / low latency /…)
• You can get down to the low-level when needed
• You care about simple designs and performance
• You want to work in a fast, high-growth startup environment that respects its engineers and customers
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. 

Benefits and Growth:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-LB1

We are looking for a senior software engineer to join our new Community Open Source Engineering team focused on growing the Quickwit OSS community and help steer the quickwit-oss collection of projects. You will play a key role in evolving quickwit & tantivy, ensuring community happiness, optimizing the release process and operations, and more. This is a hybrid role, primarily focused on Software Engineering, with a crucial component dedicated to Technical Writing, including producing blog posts and technical guides. 

Community Open Source Engineering is responsible for fostering engagement and guiding development of Datadog's open source projects outside of the core product and security research areas. The team ensures these projects deliver value to both the open source community and the company by prioritizing community engagement, maintaining software quality and security, and strategically developing new features.  As a member of a small team, you'll play a crucial role in shaping the future of Community Open Source Engineering and have a significant impact on the team's direction.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What you’ll do:

• Assist with managing the Quickwit community, responsibilities include:
• reviewing external contributions and ensuring codebase quality
• triaging reported GitHub issues
• answering questions on GitHub Discussions
• Help define the project governance. Establish contribution guidelines and continuously improve processes to minimize friction for all stakeholders.
• Collect data to assess project health and usage, identifying areas for improvement.
• Actively evangelize Quickwit to expand its reach and impact.
• Advance the Quickwit roadmap. For example, propose and implement architectural changes to facilitate contributions and reduce maintenance burdens, as well as develop highly requested features.

Who you are:

• You love talking with users and interacting with the open source community 
• You have experience managing an OSS project with a community of (internal and external) users and contributors
• You ship fast, overcome blockers with a practical mindset, and cut through ambiguity
• You have at least 3-5 years of experience with systems-level programming and preferably using Rust (or similar language)
• You are a proficient written communicator, with the ability to clearly articulate thoughts across a team
• You are passionate about developer experience and reducing friction for maintainers and contributors
• You have experience with GitHub Actions and/or release management for complex software projects
• You’ve worked on extract-transform-load tools or stream processing systems

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Competitive global benefits
• Giving programs

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

We are Datadog's in-house product experts. The technical solutions team enables Datadog's worldwide growth by educating potential clients and ensuring that existing customers are happy and successful. We share our technical and product expertise with customers through demos, presentations, technical evaluations, and ongoing support. Technical solutions is a growing global team that collaborates constantly to share knowledge and continuously advance our technical skillset.

The Logs family of products represents a strategic evolution in how organizations manage and analyze logs at scale while meeting stringent data residency, compliance, and cost requirements. The Product Solutions Architect (PSA) will work closely with Datadog customers, Logs Product team, and the CloudPrem product team to architect, implement best practices, and drive adoption of the log management suite.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Serve as the Product Expert on Logs, CloudPrem, and Observability Pipelines
• Offer guidance on architecture choices, data collection, and best practices to large Enterprise customers as they adopt these Datadog services across the organization at scale
• Design and validate hands-on, production-ready architectures spanning cloud infrastructure, Kubernetes platforms, and customer-managed environments
• Actively identify opportunities to simplify, optimize, and standardize deployment patterns, reducing operational complexity for customers through architectural guidance and best practices.
• Be a Trusted Advisor to Product Management by providing high quality feedback based on your field experience working closely with our Support, Partners, and Customers
• Capture repeat customer use cases and solutions and in turn publish Reference Architectures, automation patterns, and Implementation Guides for Datadog prospect and customer consumption
• Build world class training material, solutions briefs, blogs, and documentation to the wider Datadog field technical teams over the latest product features and capabilities
• Collaborate with other teams at Datadog including Marketing, Sales, Community, Documentation, and Evangelism to ensure the success of the Log Management product family
• Exercise the products you represent by building hands-on proof of concepts, small scale deployments  to validate solutions and reproduce customer environments
• Engage deeply with customers, feeling their operational pain points and driving solutions that measurably improve reliability, scalability, and day-to-day operations

Who You Are:

• Someone with strong hands-on experience with tools such as Kubernetes, including cluster architecture, networking, storage, and operational best practices.
• Someone with strong knowledge of cloud infrastructure fundamentals, including networking, compute, storage, and distributed system design.
• Someone familiar with Security, Governance, and Compliance requirements for industry verticals
• Someone who can dive deep into our Codebase, SDKs, and GitHub workflows to understand, debug, and influence product behavior
• Experience with languages like Python and/or Rust, and file formats such as JSON and YAML
• A self-starter, tech-savvy professional, who is comfortable working with bleeding edge technology and processes, even if it hasn’t been well defined
• A deep learner and a Subject Matter Expert today that has built advocacy and knowledge across multiple teams with the knowledge you possess 
• Fluent in understanding the observability market domain, competition, technological shifts, and customer needs
• Someone who values getting things done, taking ownership from problem definition through implementation and outcome
• Highly detail-oriented, especially when working on architectures, documentation, and customer-facing solutions
• A person who possesses exceptional listening and consultative skills, for both internal and external audiences
• Experienced working with customers in high pressure situations where the solution is sometimes not obvious - caring about customer pain and motivated to solve hard problems on their behalf
• Able to sit up to 4 hours, traveling to and from client sites 
• Able to travel via auto, train, or air up to 40% of the time 

Bonus Points:

• Successful track record with 5+ years experience working as an Architect or Consultant 
• Experience using Datadog and/or other observability tools in an SRE or DevOps capacity
• Have recent experience deploying solutions in production environments at scale
• Strong knowledge or experience with Splunk, Elastic, Opensearch, SumoLogic, Vector, Cribl, Quickwit

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Best-in-breed onboarding
• Generous global benefits
• Intra-departmental mentor and buddy program for in-house networking
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• An inclusive company culture, able to join our Community Guilds and Inclusion Talks

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

As a Cloud Security Engineer you will partner with different stakeholders across the organization to secure our cloud infrastructure. As part of the Platform Security organization we secure the building blocks of Datadog’s applications and infrastructure. We do this by building solutions to solve systemic risks and combine an approach of making the secure path easier and the insecure path harder to secure and accelerate the business. 

We regularly partner with the most bleeding edge internal products and are working to solve and build solutions to enable our safe usage of AI. We also develop AI based solutions to enable security at scale.

We are looking for a Service Mesh and Kubernetes focused security specialist to help round out an incredibly strong infrastructure security focused group. You will rotate through a variety of internal projects and gain deep exposure to Datadog’s infrastructure.

At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What You’ll Do: 

• Solve our most challenging cloud infrastructure security problems starting with our core building blocks and golden paths.

• Enable our engineers to build and ship secure solutions quickly.

• Build and extend Datadog’s Platform Security solutions.

• Leverage and influence the direction of Datadog’s products to secure our infrastructure, and provide internal feedback that enables our teams to improve the products for ourselves and our customers.

Who You Are: 

• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent professional experience.

• Passionate about advocating for and implementing solutions to complex problems, at-scale, in a large multi-cloud environment.

• You don’t want to just provide security recommendations, you want to help implement solutions to solve systemic issues. 

• You have prior experience in Software Engineering, Systems Engineering, Network Engineering, or Cloud Security.

• You have experience developing, deploying, and extending service mesh systems  in production Kubernetes environments.

• Software engineering background with hands-on code review experience; Go, Python, or Rust

• Experience with one or more Kubernetes PKI & network technologies (Consul, Envoy, Cillium, Vault, Istio, etc.).

• Experience with one of the major cloud providers (AWS, Azure, GCP) and Kubernetes.

• Able to work both independently and collaboratively, willing to work in a fast paced, high growth environment.

• You stay updated with modern security best practices, technologies and emerging threats.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. 

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)

• Continuous professional development, product training, and career pathing

• Intradepartmental mentor and buddy program for in-house networking

• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

• Access to Inclusion Talks, our Internal panel discussions

• Free, global mental health benefits for employees and dependents age 6+

• Competitive global benefits

Datadog is committed to open source. It starts with our Agent, tracers, libraries, and SDKs all being open source. But it goes so much further. From open standards to open collaboration, it’s a core part of who we are and what we do. As part of the Open Source Program Office (OSPO), you will be focusing on building tools, documentation, policies, etc. that improve how our engineers interact with Open Source projects (our own and third-party projects), as well as representing Datadog in Open Source communities. 

What You’ll Do: 

• Build and maintain tools that ease building and releasing Open Source software at Datadog
• Build and maintain tools that ease ingesting Open Source software at Datadog
• Create developer facing documentation and training materials around contributing to open source and maintaining open source projects
• Interface with open source communities to understand problems and trends in the market
• Create tools to automatically analyze and report on Open Source repositories trends and statistics
• Interface with [Datadog Open Source Partners] (Datadog for Open Source Projects | Datadog ) candidates and recipients to select new projects for the program and ensure that the program stays useful
• Research and write content for the [Datadog Open Source Hub] (Open Source Hub )
• Contribute to the product through feedback (bugs or product enhancements suggestions), documentation, or code

Who You Are: 

• Approximately 3+ years of experience as a software developer, SRE, or other practitioner.
• You are fluent in reading and writing one or more languages, e.g. Python, JavaScript, Java, Go, C#, Rust, and so on.
• You love building tools that make other engineers more productive.
• You have empathy, collaboration, and communication skills to cultivate strong relationships across various teams in the organization.
• Bonus: You are (or have been) an active member of an open source community.
• Bonus: You are familiar with different Open Source licenses and Contributor License Agreements 

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.  

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

 Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Remote

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production.

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.

We are focused on two research areas:

1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
2. Trained Agents for Observability-- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You'll Do:

• Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling
• Implement models, run experiments at scale, and profile for reliability, performance, and cost
• Build simulation environments and replay infrastructure for agent training and evaluation
• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery
• Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity
• Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Datadog's products and to harden prototypes into reliable services
• Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts

Who You Are:

• You have depth in distributed computing, RL Infra, and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus
• You are proficient in Python, familiar with a systems language (e.g., Rust, C++, or Go), and comfortable with modern cloud and data infrastructure
• You have practical experience implementing and operating ML training and inference systems (e.g., PyTorch or JAX), including containerization, orchestration, and GPU acceleration
• You have practical experience with large-scale model training and fine-tuning, including frameworks like Megatron-LM, DeepSpeed, SkyRL, VeRL, or TorchTitan, and techniques such as SFT, RLVR, RLHF, and efficient inference (quantization, speculative decoding)
• You can explain design and performance trade-offs clearly to both technical and non-technical audiences
• You have experience supporting or contributing to research publications

Bonus Points (any of the following):

• You have strong software engineering skills with experience in domains such as observability, SRE, or security
• You have experience bridging research prototypes and real-world product applications, especially with large foundation models, world models, or RL-trained agents
• You have a passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
• You have hands-on experience with GPU programming and optimization, including CUDA
• You have experience writing production data pipelines and applications
• You have experience building simulation or sandbox environments for agent training

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Competitive global benefits 
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
• Opportunity to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

About Datadog: 

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.

#LI-Hybrid

About the role:

We are looking for experienced software engineers to join our Platform organization. We build the foundational primitives that accelerate product development across Anthropic, and own infrastructure and systems that teams depend on to ship reliably and at scale - either internally or by hundreds of thousands of external global users and companies at all stages. 

You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make architectural decisions that shape how Anthropic builds and scales its products. You'll partner directly with research to productize cutting-edge capabilities, and will have lasting impact on the platform that hundreds of thousands of companies and internal/external engineers depend on every day.

We have multiple teams that are currently hiring. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the backend product efforts where they'll have the greatest impact and growth potential. 

What you'll do: 

Platform Acceleration: We work on maximizing the developer productivity of product engineers at Anthropic. You'll architect and optimize the critical development infrastructure that powers our AI product development, including dev environments, observability, and CI/CD pipelines. You’ll partner closely with product teams to understand their development workflow and eliminate friction points. Your work will have an extraordinary multiplier effect, enhancing productivity across our entire product organization and accelerating our mission.

Service Infra: we build and maintain the core infrastructure that powers Anthropic's engineering organization, from service mesh and observability systems to deployment pipelines and shared libraries. Our work enables product teams to build and operate reliable services at scale, making us a critical force multiplier across the entire company.

Multicloud: We build and maintain the infrastructure that enables Anthropic to operate across multiple cloud providers. We focus on cloud-agnostic tooling, cross-cloud networking, and multi-region deployments.

Auth & Identity: We build and maintain the critical infrastructure that powers identity and authentication across Anthropic's product suite. We work closely with product teams, security, support, and trust & safety as customers. We create scalable solutions for user authentication, authorization, role-based access control, and single sign-on that form the backbone of our company's identity management operations. We maintain a user-centric approach, building reliable systems that our users and company can depend on as we tackle complex challenges at the intersection of security, scalability, and user experience.

Connectivity: Our mission is to make Claude the most connected AI. We own the MCP proxy that routes every tool call and the OAuth and token management that keeps connections authenticated. We're also the core contributors to the MCP spec — now an open standard under the Linux Foundation — and maintain the official Python and TypeScript SDKs. You'll work on problems where reliability and enterprise trust are the bar: token refresh at scale, admin controls that let IT govern what agents can do, proxy infrastructure that stays up when partner servers don't. We ship for claude.ai, Claude Code, Cowork, and the API. Relevant experience includes OAuth, API gateways, multi-tenant platforms, building for enterprise, and MCP.

API Distributability: The Claude API today is a rapidly growing platform serving developers and enterprises at scale—but reaching the next tier of enterprise customers requires transforming how and where we deploy it. The Distributability team owns that transformation: making the Claude API a cloud-native, managed product that runs wherever our customers need it, cross-cloud and on Anthropic's own infrastructure, with the enterprise-grade security, compliance, and operational capabilities to support it.

Platform Intelligence: We build the training systems that adapt Claude to specific customer workloads. The core problem is task-specific adaptation: getting the right intelligence, cost, and latency profile for a particular use case, and building toward systems where that adaptation can deepen as the customer's usage grows. We work closely with research on training methods and with agent platform teams on data paths. Relevant experience: ML training infra, production ML pipelines, backend engineering. Finetuning experience is a plus.

You might be a good fit if you:

• Have a minimum of 8 years of practical experience as a backend or platform engineer building scalable distributed systems, ideally operating at tech lead level or equivalent 
• distributed systems, cloud-native products, developer tools, or external developer facing products
• Have strong fundamentals in service-oriented architectures, networking, and systems design, ideally having owned both the technical vision and execution of a foundational platform system end to end 
• Are proficient in Python, Go, Rust, or similar systems languages
• Have experience with cloud infrastructure (GCP, AWS, or Azure), container orchestration (Kubernetes), and/or multi-cloud networking 
• Take full ownership of your work—from design through deployment and operations
• Can navigate ambiguity and make sound technical decisions independently, and have ideally operated in 0 to 1 and more mature team or company settings 
• Take a product-focused approach to platform work and care about building solutions that are robust, scalable, and easy to use
• Care about building systems that other engineers love to use

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Location Preference: Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team. 

Staff Software Engineer - AI Research Infrastructure

P-1215

At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions.

The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.

Job Description

As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security.

You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support.

The Impact you will have

As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: 

• Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems)
• Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring.
• Create tooling that improves research developer productivity, such as experiment management systems, CI/testing infrastructure for research code, and workflows that reduce iteration time.
• Influence the long‑term roadmap for research computation, shaping how Databricks AI Research train, evaluate, and ship models to customers.
• Serve as a technical mentor and force multiplier for other engineers working on compute, infra, and AI systems.

What We Look for

• BS/MS or PhD in Computer Science or related field
• 5+ years of software engineering experience, including substantial time working on large‑scale distributed systems or infrastructure.
• Have deep experience with building and operating distributed systems, data pipelines, or large‑scale backend services, ideally involving GPUs, clusters, or major cloud providers.
• Are proficient in one or more systems programming languages (e.g., C++, Rust, Go, Java, Scala) and can design, implement, and debug complex services.
• Have built or significantly contributed to cluster schedulers, resource managers, or large‑scale job orchestration systems (e.g., Kubernetes, Slurm, Ray, custom internal systems).
• Understand modern ML training and inference workflows (e.g., distributed training, model parallelism, fine‑tuning, evaluation), even if you’re not primarily a research scientist.
• Can move fast and be pragmatic in getting things done, while caring about operational excellence. Have driven complex systems from prototype to stable, well‑owned services.
• Communicate clearly with both researchers and engineers, and enjoy translating between research needs and infra realities.

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

DRW is seeking an exceptional Software Engineer to join a newly created team responsible for designing and developing tools and infrastructure used by Traders and Quantitative Researchers. In this role, you will be collaborating with people across the firm to provide cutting-edge technology solutions that expand our existing trading platform, all while promoting software engineering best practices. You will be presented with an opportunity to work on high profile projects that have an impact across the organization.

Responsibilities:

• Manage the day-to-day technology requirements of our proprietary trading systems
• Accelerate trading and research analysis workflows by utilizing and expanding on DRW’s in-house infrastructure and improve productivity
• Evaluate new technologies and identify/deprecate legacy code to improve on the current technological stack
• Collaborate with other engineers, traders, and researchers to design custom frameworks or end-user solutions
• Write performance sensitive code and user-friendly APIs to facilitate access to our in-house data analysis infrastructure
• Lead design discussions and conduct code reviews to maintain high standards and quality of written code

Requirements:

• Bachelor’s or Master’s degree in Computer Science or related Engineering discipline
• 5+ years of experience building demanding, live systems in Python
• In-depth knowledge of Python and keeps up with current standards (3.11 onwards)
• Up-to-date knowledge of the Python ecosystem: third party libraries, packaging in-built Python libraries
• Knowledge of a systems language such as C, C++, Rust, etc.
• Solid understanding of data structures
• Strong verbal and written communication skills
• Must car about the user-experience/UX component
• Knowledge of CI software, such as TeamCity or Jenkins
• Familiarity with Linux
• Ability to mentor others and lead small projects

Bonus points:

• Experience in the trading industry
• Familiarity with the scientific python stack
• Understanding of the latest in python package management

The annual base salary range for this position is $150,000 to $180,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

[#LI-SK1] 

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

The UP - Analytics - Middle Office team is looking for an outstanding Senior Software Engineer with diverse technical and interpersonal skills to join us in the next step of our development as we significantly expand our technical capabilities, enabling us to have an even greater impact across the firm. This team build and maintains services which: 

• calculate analytic measures across many asset classes
• predict theoretical market outcomes based on user-defined what-if scenarios
• return analytics for any time horizon

We collaborate closely with risk managers and trading desks to measure option and rate sensitivities and create applications to monitor, manage, and report analytic measures. We're looking for talented software engineers with proven experience in the trading and finance industry. 

Responsibilities: 

• Partner closely with stakeholders and quantitative teams to design and implement complex financial modeling solutions
• Leverage your expertise in finance and quantitative analysis to architect robust, scalable analytics systems
• Write readable and well-tested code in multiple languages, paradigms, and platforms
• Collaborating with various business, development, IT, and trading support teams
• Provide on-call support as part of our team-wide rotation
• Keep open communication with our users for support, testing, and ensuring understanding of their needs

Qualifications: 

• Experience using applied mathematics (linear algebra, optimization, numerical methods)
• Strong understanding of statistics and probabilities
• Quantitative modeling of financial products and derivatives
• Fluent in functional, object-oriented, or imperative programming languages
• Competent in distributed version control, CI/CD, Docker, and Kubernetes

Familiarity with: 

• One of the following programming languages: Elixir, Python, Java, C#
• One systems level programming language: C, C++, Rust
• Pricing, risk management, and portfolio analytics
• Exchange-traded financial assets, statistics, or financial engineering
• Data structures and design/analysis of algorithms

Personal traits: 

• Possesses the ability to learn, adapt and grow
• Demonstrates personal humility, respect for others, and trust in their teammates
• Capable of independently driving projects to completion but prefers collaborating with teammates
• Excellent problem-solving and debugging skills, but has better listening and communication skills

The annual base salary range for this position is $200k to $250k depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

[#LI-SK1] 

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

About the Team

We are a fast-moving and progressively-thinking team that intimately collaborates with traders and quantitative researchers to implement, refine and deploy alpha signals, evaluate and maintain trading tools, improve our research pipeline, while ensuring that our code bases remain nimble, maintainable and extensible.

We embed deeply with our customers to solve high-impact, complex problems.

We move quickly when we prototype, and we tread lightly when we ship to production.

We operate at the intersection of ensuring customer success and creating quality products that stand the test of time.

About the Role

As a Research Engineer, you will be an integral member of a systematic trading team comprised of experienced technologists, quantitative researchers, and traders. You will:

• Work directly with researchers to implement research studies, simulate trading strategies, and develop algorithms to compete in financial markets.
• Embed with trading desks, work intimately with traders and portfolio managers, learn about their problem spaces, extract domain models, and build ergonomic, performant and extendable engineering solutions
• Collaborate with other engineering teams to accelerate the research lifecycle from strategy prototype to production deployment
• Evaluate new technology and improve our technology stack on a continuous basis

What you bring to the team:

• Bachelor’s degree in Computer Science, Computer Engineering, or related field
• A minimum of 2+ years’ experience using Python in a production setting
• A strong understanding of quantitative finance and mathematics
• Experience with linux-based, large-scale, concurrent, high-throughput, low-latency software systems
• A track record of working directly with end customers, scoping and delivering production systems in fast-moving and ambiguous environments
• Exceptional interpersonal skills - you communicate clearly with stakeholders as well as other engineers, fostering a collaborative, supportive working environment.
• Experience with low-level systems programming languages (C/C++, Rust, Zig) is a plus
• Experience with functional programming is a plus

The annual base salary range for this position is $175,000 to $225,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

#LI-BL1 

About the Role

Our mandate is to make inference deployment boring and unattended.

Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium — and every model update must reach production safely, quickly, and without disrupting service. We're building the systems that make inference deployment continuous and unattended.

As a Software Engineer on the Launch Engineering team, you'll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic — your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You'll build systems that navigate these constraints — orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.

If you've built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.

Responsibilities

• Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions
• Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes
• Extend deployment observability — dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy"
• Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism
• Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity
• Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement
• Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems

You May Be a Good Fit If You Have

• 5+ years of experience building deployment, release, or delivery infrastructure at scale
• Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines
• Experience with deployment systems where resource constraints shape the design — whether that's fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows
• A track record of building automation that measurably improves deployment velocity and reliability
• Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
• Comfort working across the stack — from backend services and databases to CLI tools and web UIs
• Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners

Strong Candidates May Also Have

• Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)
• Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)
• Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
• Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)
• Experience with Python and/or Rust in production systems

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP, Azure)
• Python or Rust

Representative projects:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Why this role?

Warp is hiring an Analytics Engineer to own the data vertical — a high autonomy role at the center of how we understand our product, users, and business. You'll work across every function at Warp: product, engineering, growth, revenue, and go-to-market.

Warp is a fast-growing AI developer tool. Our data footprint reflects that growth — we ingest telemetry from our apps and servers, LLM conversational data, transactional production databases, GTM pipelines, and a range of third-party sources. Your job is to turn that raw data into the reliable metrics and deep insights that drive real decisions.

This isn't a role where you slot into an existing system. You'll own the data model, define how we measure what matters, and set the standard for how analytics gets done at Warp. That means building durable infrastructure and shipping high-impact analysis — often at the same time.

If you want to shape how a company at scale thinks about its data from the ground up, this is that role.

As an Analytics Engineer, you will...

This is a full-stack data role that moves across the data stack and touches all parts of Warp’s business. Your work will fall into three broad areas:

Operations

• Define, build, and visualize core product and business metrics
• Design and analyze A/B experiments to measure the impact of product changes
• Adapt data models to support new product features, pricing models, and business initiatives
• Partner with GTM teams to surface product-qualified leads, measure engagement, and support billing and revenue analytics

Infrastructure

• Build and maintain canonical data models that serve as reliable sources of truth
• Write production ETL to ingest new data sources and reverse-ETL to push data to downstream consumers
• Develop AI agents to automate pieces of the data team’s workflows
• Establish data quality monitoring and reliability standards
• Build tooling that enables self-serve analytics across the company

Research

• Conduct deep ad-hoc analysis to understand user behavior, model business mechanics, and uncover growth opportunities
• Dig into questions like: How does Warp expand within companies? What is the optimal pricing structure? How do we identify potential enterprise leads? What characteristics of AI interactions drive churn?

You May Be A Good Fit If...

• You have 6+ years of experience in an analytics-focused data role
• You’re highly fluent in SQL and dbt on top of a modern warehouse like BigQuery, Snowflake, or Redshift
• You’ve built dashboards and visualizations with BI tools like Hex, Looker, Mode, Tableau, or Metabase
• You care deeply about the business decisions your data informs—not just building the pipelines behind them
• You balance business intuition with an engineering mindset: writing efficient, scalable queries; thinking about cost and compute across the data stack; and working with version control, CI/CD, and testing
• You have a bias for action and are comfortable operating with autonomy—you can identify opportunities across the company and proactively build solutions
• You’re an effective and creative communicator who can translate complex analysis into actionable insights for stakeholders with varying levels of data fluency
• You’re deeply curious—when a metric moves unexpectedly, you dig until you understand why
• You’ve integrated AI into your daily workflows; at Warp we lean heavily on agents to accelerate and automate our work

Bonus:

• Experience with the GCP data stack (GCS, BigQuery, Dataflow, Dataproc)
• Production-grade experience with Python
• Experience on a data team at an early-stage startup
• Familiarity with Warp’s platform

At Warp, we are dedicated to building a diverse, inclusive, and authentic workplace. So if you’re excited about this role, but your past experience doesn’t align perfectly with every qualification in this job description – we encourage you to apply anyways! We are a community of curious learners, and most of us are learning some skills for the first time (like our engineers learning to program Warp in Rust). You might be just the right candidate for this or other roles.

If you're feeling playful, try out our optional hiring challenge and submit your answers with your application: https://github.com/warpdotdev/hiring-challenge

Salary Transparency

Total compensation at Warp consists of two parts: 1) a competitive base salary, and most importantly, 2) meaningful equity.

When we find the right person, we try to put our best foot forward with an offer that excites you. We consider what you’d like to be paid, the skills and level of experience you bring, what similar jobs pay, and make sure there’s equal pay for equal work among those you’ll be working with. The budgeted compensation amount for this role is targeted at $180,000 - $210,000.

In addition to salary, all employees receive further compensation in the form of equity in the company. This is a meaningful stock option grant with a four-year vesting period and one-year cliff. Your equity is where most of the significant upside potential is. Comparing startup equity is always a bit tricky, so we’re happy to walk you through different valuation scenarios at the offer stage in order to help paint a clearer picture of the upside.

Final total compensation is determined by multiple factors including your experience and expertise and may vary from the amounts listed above.

What We Offer

• Competitive Salary & Meaningful Equity – we will stretch to get the right talent on board
• Full Medical, Dental, and Vision Benefits for employees (80% coverage for dependents)
• Flexible remote-first culture, with optional office spaces in NYC and SF for folks who want to work together IRL 
• Pre-tax FSA Health Savings Plan
• Pre-tax Commuter Benefit
• 20-days of Paid Time Off
• Unlimited Sick Time Off
• 12 US Holidays
• 16 weeks of paid Parental Leave for both birthing and non-birthing parents
• Twice-a-year company retreats
• Monthly gym and internet stipend
• Guideline 401(k)
• Complimentary OneMedical membership

Individuals seeking employment at Warp are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.

Location: Remote 

Position Summary: 

Join our Scalability team as a Senior Scalability Engineer focused on streaming and realtime systems. In this role, you will own the architecture and expansion of our streaming data infrastructure, including WAL-based database replication, change data capture (CDC) systems, and realtime event processing. Your work will handle systems processing hundreds of thousands to millions of records per batch while maintaining strict ordering guarantees and exactly-once semantics. Working closely with our clients and cross-functional teams, you'll build foundational streaming infrastructure while partnering directly with product teams to implement realtime capabilities that power critical business workflows. 

Position Responsibilities: 

In this role, you'll provide technical leadership while delivering hands-on solutions for high-impact performance challenges. You will: 

• Own streaming infrastructure: Design, implement, and expand WAL-based replication systems that process database changes through Kinesis to Snowflake and Redshift, handling millions of records while maintaining strict ordering and delivery guarantees. 
• Build CDC systems: Architect and implement change data capture infrastructure for cross-platform data synchronization, enabling realtime analytics and event-driven workflows across the organization. 
• Develop shared libraries: Create reusable Kinesis/SNS consumer patterns and libraries used across multiple teams, establishing best practices for event processing, error handling, and observability. 
• Partner with product teams: Work directly with teams to design and implement realtime data processing solutions tailored to their business needs, providing technical guidance and hands-on support. 
• Ensure data reliability: Implement exactly-once processing semantics, dead letter queues, retry strategies, and monitoring to guarantee data integrity across streaming pipelines. 
• Build observability: Develop monitoring, alerting, and dashboards for streaming pipelines to track throughput, lag, data quality issues, and system health using the LGTM stack.  
• Demonstrate technical leadership: Mentor engineers on streaming architecture patterns, lead design reviews for event-driven systems, and represent the Scalability team in cross-functional planning. 
• Make strong architectural choices through careful evaluation and prior experience with distributed systems.
• Responsible for adherence to the Capital Rx Code of Conduct, including reporting of noncompliance. 

Required Qualifications: 

• 10+ years of software engineering experience with demonstrated progression into technical leadership roles. 
• 3+ years of experience leading technical initiatives, architecting distributed systems, or serving as a subject matter expert on streaming infrastructure. 
• Strong expertise in Python (Flask/SQLAlchemy) for production applications. 
• Deep PostgreSQL knowledge: Understanding of write-ahead logs, replication, logical decoding, and change data capture mechanisms.
• Production streaming experience: Proven track record building and operating high-throughput streaming systems using Kinesis, Kafka, or similar event streaming platforms. 
• Distributed systems expertise: Strong understanding of ordering guarantees, exactly-once semantics, partition strategies, backpressure handling, and fault tolerance patterns. 
• AWS experience: Production experience with Kinesis, S3, SNS/SQS, Lambda, ECS, and data pipeline orchestration.  
• Data warehouse knowledge: Experience loading data into Redshift, Snowflake, or similar analytical databases.  
• Systems thinking: Ability to design resilient, observable streaming architectures that balance throughput, latency, and reliability.  
• Collaboration and communication: Strong written and verbal communication skills with ability to work autonomously while driving proactive collaboration in a remote environment. 

(Optional) Preferred Qualifications:   

• Rust development experience or strong interest in learning Rust for high-performance systems.
• Infrastructure as code: Experience with Terraform or similar IaC tools for managing cloud infrastructure. 
• Observability tools: Hands-on experience with Grafana, Prometheus, Loki, or similar monitoring/alerting platforms.  
• Event-driven architectures: Background designing event sourcing, CQRS, or other event-driven patterns.
• Previous Pharmacy Benefits Manager (PBM) or healthcare technology experience. 

This range represents the low and high end of the anticipated base salary range for the NY - based position. The actual base salary will depend on several factors such as: experience, knowledge, and skills, and if the location of the job changes. 

Nothing in this position description restricts management’s right to assign or reassign duties and responsibilities to this job at any time. 

Location: Remote 

Position Summary: 

Our Scalability team as a Senior Scalability Engineer focused on observability platform development and engineering productivity. In this role, you will define, own, and build Judi Health's organization-wide observability strategy, tooling, and platform products. Beyond maintaining infrastructure, you'll architect and develop a custom observability platform that gives engineering teams powerful, fast, and cost-effective visibility into every layer of our infrastructure—from application logs and metrics to distributed traces. You'll build production-grade internal products using React/TypeScript frontends with Python and Rust backends, creating tools that fundamentally improve how engineers at Judi Health debug, monitor, and optimize their systems. Working closely with leadership and cross-functional teams, your work will be foundational to platform stability, performance optimization, and developer productivity across our rapidly growing healthcare platform. 

Position Responsibilities: 

In this role, you'll own the observability infrastructure that powers our engineering organization. You will:  

• Architect observability platform: Design, implement, and maintain the LGTM stack (Loki, Grafana, Tempo, Mimir/Prometheus) as the primary observability platform across all engineering teams, making architectural decisions that balance cost, performance, and developer experience.
• Build internal observability products: Design and develop production-grade internal platform products with React/TypeScript frontends and Python/Rust backends that provide engineers with powerful log search, metrics visualization, and trace analysis capabilities.
• Develop custom log indexing systems: Architect and build high-performance log indexing solutions using Rust that process logs and provide sub-second search across billions of log lines at a fraction of the cost.
• Integrate SQL analytics for logs: Design and implement solutions leveraging AWS Athena or similar SQL query engines (DuckDB, ClickHouse) for ad-hoc log analysis and historical queries, enabling engineers to run complex SQL queries over S3-based log data for deep investigations and trend analysis. 
• Create advanced query interfaces: Build sophisticated web interfaces that allow engineers to query logs, metrics, and traces with features like saved queries, query templates, correlation analysis, and pattern detection, supporting both full-text search and SQL-based analytics. 
• Balance cloud-native and open-source: Architect solutions that thoughtfully leverage both AWS-managed services (CloudWatch, Athena, Kinesis) and open-source tooling (LGTM stack, Quickwit) to optimize for cost, performance, and operational flexibility based on use case requirements. 
• Integrate AWS observability: Design seamless integration between AWS CloudWatch Logs/Metrics and our custom observability platform, providing unified visibility across managed and self-hosted infrastructure. 
• Build intelligent alerting: Develop smart dashboards, monitors, and alerting systems that reduce noise, detect anomalies, and help teams respond to incidents quickly. 
• Partner with engineering teams: Work directly with product teams to integrate observability into their services, establish logging and metrics standards, and instrument code effectively, serving as the observability subject matter expert. 
• Enable performance optimization: Provide the observability foundation that allows the Scalability team to identify performance bottlenecks, track optimization impact, and measure platform stability with data-driven insights. 
• Establish observability standards: Define and document comprehensive observability standards including structured logging patterns, metric naming conventions, trace instrumentation, dashboard design principles, and query best practices. 
• Drive platform adoption: Lead workshops, create documentation, and build self-service tooling that democratizes observability across engineering, making it easy for teams to adopt best practices. 
• Demonstrate technical leadership: Mentor engineers on observability practices, lead architecture reviews for instrumentation approaches, and represent the Scalability team in cross-functional planning. 
• Work in an Agile/Scrum environment to continually deliver value to stakeholders and clients. 
• Code of Conduct: Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance. 

Required Qualifications: 

• 10+ years of software engineering or infrastructure engineering experience with demonstrated progression into technical leadership roles. 
• Several years of experience leading technical initiatives, building platform products, or serving as a subject matter expert on observability infrastructure. 
• Strong experience with React/TypeScript for frontend development and Python (Flask/SQLAlchemy) for backend services. 
• LGTM stack expertise: Deep production experience with Loki, Grafana, Tempo, and Prometheus/Mimir for logs, metrics, and distributed tracing at scale. 
• AWS observability: Extensive experience with AWS CloudWatch Logs and Metrics, including custom metrics, log insights, dashboard creation, and integration patterns. 
• SQL analytics for logs: Production experience with SQL-based log analytics using AWS Athena, DuckDB, or similar query engines for analyzing structured and semi-structured data at scale. 
• Cloud-native and open-source balance: Demonstrated ability to architect solutions leveraging both managed cloud services and open-source tooling, understanding trade-offs between operational overhead, cost, flexibility, and vendor lock-in. 
• Search and indexing experience: Hands-on experience building or operating search systems using OpenSearch, Elasticsearch, Lucene, Tantivy, or similar search and analytics engines. 
• Performance-critical systems: Experience building high-performance systems that process large volumes of data efficiently (millions of log lines, high-cardinality metrics). 
• Systems thinking: Deep understanding of distributed systems, microservices architectures, and the complex observability challenges they present. 
• Data at scale: Proven track record handling high-volume structured and unstructured logging data, identifying patterns, and building efficient search/query solutions that perform well under load. 
• Product mindset: Ability to build internal platform products that engineers love to use, with attention to UX, performance, and reliability. 

Preferred Qualifications: 

• Rust development experience: Production experience with Rust for building high-performance data processing, indexing, or search systems. Strong interest in learning Rust is acceptable if combined with systems programming experience in C/C++/Go. 
• Infrastructure as code: Experience with Terraform for managing observability infrastructure and AWS resources. 
• Additional observability platforms: Experience architecting or operating Datadog, New Relic, Splunk, or other enterprise observability platforms. 
• Advanced query languages: Deep expertise with PromQL, LogQL, SQL optimization, and query optimization for high-cardinality data. 
• Columnar storage formats: Experience with Parquet, ORC, or other columnar storage formats for efficient log storage and analytics on S3. 
• Incident management: Experience designing incident response workflows, postmortem processes, and SLO/SLI frameworks that drive reliability improvements. 
• Cost optimization: Track record of reducing observability costs while maintaining or improving capabilities (e.g., CloudWatch → S3/custom indexing migration). 
• Data pipelines: Experience with streaming data pipelines, ETL processes, or real-time data processing. 
• Distributed tracing: Deep knowledge of OpenTelemetry, Jaeger, Zipkin, or distributed tracing architectures. 
• Git expertise and experience working in a mono repository. 
• Previous Pharmacy Benefits Manager (PBM) or healthcare technology experience. 
• Experience building developer tools or internal platforms that improve engineering productivity. 

This range represents the low and high end of the anticipated base salary range for the NY - based position. The actual base salary will depend on several factors such as: experience, knowledge, and skills, and if the location of the job changes. 

Nothing in this position description restricts management’s right to assign or reassign duties and responsibilities to this job at any time. 

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: Software Engineering (SWE)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Reports to: CRO (until VP, Field Engineering is hired)

Compensation: OTE $260–320K (Senior) or $325–400K (Staff) · 75/25 base/variable split · Equity

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the software engineering domain. Every major lab is racing to push the frontier on code generation, agentic software engineering, and SWE evaluation. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

This is a Field Engineering founding role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

What You'll Do

1) Technical discovery — lead the technical conversation on every qualified SWE opportunity

• Partner with Research Partners to run the technical track with AI researchers and research leads.
• Understand what they're training, what they're evaluating, where their pipeline breaks, and what a Turing-built artifact looks like in practice.
• Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.

2) Solution architecture — translate lab needs into scoped Turing deliverables

• Map capability goals to Turing's offering shapes: custom human expert data, off-the-shelf datasets, and managed talent.
• Author technical proposals that AI researchers accept and the Production Engineering team can execute without a rewrite.

3) Prototyping and demo-building — prove the approach before contract

• Build sample eval tasks, reference dataset slices, graded trajectories, and working agentic scaffolds.
• Expect to write real code, not mock-ups. The demo has to run.

4) POC ownership — take paid pilots from kick-off to scale-up decision

• Design the measurement plan, define success criteria, own the cadence.
• The outcome you are measured on: POC converts to production contract.

5) R&D interface — be the pre-sales channel between GTM and R&D for SWE

• Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
• Maintain a predictable collaboration cadence that R&D teams trust.

6) Playbook building — codify what works so future hires scale faster than you did

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns.
• Own the SWE section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in software or ML engineering, with meaningful production experience on code-generation, code-understanding, or developer-tooling systems.
• Hands-on fluency in Python and modern LLM tooling; comfort reading and writing across at least one other major language (TypeScript, Go, Rust, or Java).
• Experience designing or working with evaluations for code models — benchmarks, rubric design, grader reliability, eval construction.
• Experience with large codebases, agentic SWE systems, or developer-facing AI products.
• A high written communication bar: you can produce a scoping document that a frontier lab engineer accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, AI infrastructure company, or developer-tools company shipping to AI customers.
• Experience with SWE benchmark construction (e.g., SWE-bench, LiveCodeBench, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; software engineering domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on SWE opportunities you cover is materially above the non-covered baseline; qualification bar codified; R&D and Field Engineering interface running on a predictable cadence.
• 180 days: a second Pre-Sales AI Solutions Engineer in the SWE domain hired behind you, ramping off your playbook. You spend less than 30% of your time as a solo contributor and more than 70% multiplying through the function.
  
  

Why Turing

• Work directly with the world's leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
• Real impact on the path to AGI: the datasets, evaluations, and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards, not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI, not to procurement.

How to apply

Send a Resume or CV and a short note on a technical artifact you built — ideally something customer-facing, evaluation-adjacent, or that demonstrates how you think about technical scoping. We read every submission.