What You’ll Do:

The Monolith AI Platform Engineering Team at CoreWeave is responsible for building and scaling the core platform that powers the world’s most advanced engineering simulation and AI workflows. From evolving distributed architecture to enabling the next generation of AI-agentic applications, this team delivers the performant, reliable, and scalable foundation trusted by the world’s largest engineering companies.

About the Role:

We are seeking an Engineering Manager to lead the Monolith AI Platform Team and help us scale this mission-critical platform. This individual will grow and guide a team of engineers as we push the boundaries of simulation technology and AI-agentic applications. As the Manager of this team you would have the opportunity to:

• Drive the culture and tone for how your team keeps score — both in how they support each other and how they enable the rest of Monolith AI.
• Support your people and create opportunities for them to be challenged and grow.
• Develop a customer-centric mindset, perform regular needs analysis and prioritization, create and deliver roadmaps, and maintain a highly available and reliable platform.
• Work directly with other engineering teams to assess needs and guide their implementations and integrations with your team’s platform.
• Implement processes and standards to achieve a highly mature practice of testing, distributed systems orchestration, and platform delivery to guarantee a first-class customer experience.
• Grow, change, invest in your teammates, be invested-in, share your ideas, listen to others, be curious, have fun, and, above all, be yourself.

Who You Are:

• 7+ years of software engineering experience, with at least 3 years primarily focused on Python.
• Demonstrable experience building and/or leading software engineering teams.
• Knowledge of ML engineering principles and practices, especially implementing them within production settings.
• Experience working on data-intensive products, whether high-volume (big data) or high-scale (high-load) systems.
• Experience with cloud infrastructure (particularly AWS, Azure/GCP desirable),  including services such as Athena, Synapse, Spark, or Kubernetes.
• Experience with distributed systems and workflow orchestration (e.g. Temporal, Airflow, Kubeflow, Flyte or similar).
• A bottoms-up leadership philosophy focused on enablement, communication, organisation, and inspiration over task management.

Preferred:

• 2+ years of prior experience managing a software engineering team, including recruitment and retention.
• Experience working on AI-based or ML-powered products in a production environment.
• Distributed computing experience (e.g. Spark, Ray, or similar frameworks).
• Full-stack development experience.
• Experience with containerisation and Kubernetes in production workloads.

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.

• Mentorship Mindset: You love helping people on their journeys to become their best selves, elevating others through knowledge sharing and patient guidance.
• Pragmatic Problem-Solver: You balance perfectionism with shipping, making smart trade-offs between short-term delivery and long-term platform goals.
• Technical Excellence: You remain hands-on enough to engage meaningfully in code reviews and technical decisions, and you care about engineering craft.
• Ownership Mentality: You take full responsibility for outcomes and drive initiatives to completion.
• Customer Obsession: You understand that the platform exists to serve customers, and you develop a customer-centric mindset in everything you build.
• Startup Spirit: You’re excited about the dynamism of a fast-growing company and thrive in environments that reward curiosity, adaptability, and impact.

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 growth opportunities within the organisation 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!

What You’ll Do:

The Monolith AI Platform Engineering Team at CoreWeave is responsible for transitioning existing client environments to Monolith AI’s next-generation platform, maintaining the high velocity needed to support a rapidly growing customer base. This team works closely with Customer Success and Product to ensure migrations are accurate, meet defined business requirements, and are delivered at scale.

About the Role:

Monolith AI is seeking a talented Software Developer for an intensive 3-month migration project transitioning existing client environments to our next-generation platform. This role requires a developer who excels at rapidly understanding undocumented systems, extracting clarity from complex database structures, and building practical migration tooling in Python. You’ll work closely with our 15-person technical team (currently doubling), collaborating extensively with Customer Success and Product to ensure migrations meet defined business requirements while maintaining high velocity.

In this role, you will:

• Rapid System Understanding
  • Quickly grasp existing PostgreSQL database schemas through code inspection, team discussions, and reverse engineering
  • Understand new platform database architecture and how legacy data maps to the new structure
  • Work with pickle files and S3 object storage to understand serialized data structures
  • Extract clarity from poorly documented legacy systems through hands-on investigation
• Cross-Functional Requirements Gathering
  • Collaborate with Customer Success to understand client migration requirements and acceptance criteria
  • Partner with Product team to define and iterate on what constitutes acceptable migration outcomes
  • Balance technical constraints with business needs to establish pragmatic migration standards
  • Clarify ambiguous requirements and drive decisions on edge cases and trade-offs

• Product Context & Impact Analysis
  • Understand how database entries and S3-stored objects drive UI behavior and user workflows
  • Work with the existing team to learn system behavior through discussions and knowledge transfer
  • Identify how legacy data structures distribute across new platform tables and storage

• Migration Tool Development
  • Build Python migration scripts to convert legacy notebooks to the new platform format
  • Work with Flask and FastAPI layers to understand API interactions and data flow
  • Handle pickle file deserialization and data transformation logic
  • Implement validation against defined Customer Success requirements (not 100% data integrity)

• Velocity & Communication Management
  • Maintain rapid project pace with tight 3-month timeline
  • Provide frequent progress updates to the tech lead on development speed and blockers
  • Proactively communicate risks, timeline concerns, and decision points
  • Make pragmatic technical decisions that balance quality with delivery speed

• Testing & Validation Against Requirements
  • Test migration outputs against Customer Success acceptance criteria
  • Validate migrated notebooks meet defined business requirements
  • Identify and communicate gaps between migration output and requirements for product decisions

• Process Documentation & Knowledge Transfer
  • Document the migration process, key decisions, and data mapping logic
  • Create runbooks for executing migrations and handling common issues
  • Transfer knowledge primarily through discussions with the existing team
  • Prepare materials enabling team to execute and troubleshoot migrations post-contract

Who You Are:

• 3+ years of professional software development experience
• 2+ years working with Python in production environments
• Experience working with PostgreSQL databases and complex data structures
• Demonstrated ability to reverse engineer and understand undocumented systems
• Previous work in fast-paced, deadline-driven environments
• Programming: Strong Python proficiency including handling pickle files, data processing, and object serialization
• Databases: Solid understanding of PostgreSQL including complex data structures, relationships, and schema interpretation
• APIs: Familiarity with Flask and/or FastAPI frameworks and RESTful API concepts
• Cloud Storage: Experience with S3 or similar object storage services for reading/writing data
• System Investigation: Ability to read and understand existing codebases to extract system behavior
• Version Control: Proficiency with Git and collaborative development workflows

Preferred:

• Experience with data migration or system modernization projects
• Background working with legacy systems or technical debt remediation
• Familiarity with Jupyter notebooks or similar computational environments
• Previous contract or consulting roles requiring rapid onboarding
• Experience in startup or scale-up environments with evolving requirements
• Knowledge of data serialization formats and their trade-offs
• Prior work extracting understanding from poorly documented codebases
• Experience balancing technical perfection with business pragmatism

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.

• Rapid Learner: You can quickly understand complex, undocumented systems through code inspection and experimentation, and extract clarity from incomplete information.
• Pragmatic & Velocity-Driven: You thrive in fast-paced environments with tight deadlines, make practical trade-offs between thoroughness and speed, and maintain a strong bias toward action and delivery.
• Communicator: You proactively provide progress updates, surface blockers early, and clearly articulate technical concepts to non-technical stakeholders like Customer Success and Product.
• Collaborative: You build relationships quickly, partner effectively with Customer Success and Product to understand business requirements, and actively engage with the existing technical team to facilitate knowledge transfer.
• Owner: You take full ownership of deliverables despite a short contract timeline, and maintain composure and productivity in a dynamic, growing organisation (team currently doubling).
• Results-Focused: You prioritise work that directly impacts migration success, stay transparent about what’s achievable within timeline constraints, and are accountable when risks emerge.

Why Monolith AI?

At Monolith AI, 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 growth opportunities within the organisation 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!

What You’ll Do:

The Data Science team is focused on developing an advanced reliability platform. This system covers various aspects of data processing and analysis, including data intake, deriving meaningful metrics, identifying unusual patterns, predicting potential issues, finding slow processes in distributed systems, and using automated analysis to determine causes. We collaborate closely with internal teams like Fleet, Infrastructure, and AI Platform to enhance system stability, optimize resource use, shorten resolution times, and maintain service availability and financial performance.

About the role:

As a Senior Data & MLOps Engineer, you will design and scale the infrastructure supporting the GPU Intelligence Platform. This involves building pipelines for handling data, features, model training, and delivering insights and predictions for system health and optimization. You will transition the system from initial prototypes to a production environment operating across the fleet, focusing on scalability, separating real-time service from periodic processing, and dynamic resource management based on system load and data frequency. You will architect and deploy these scalable distributed services using orchestration technologies.

Key responsibilities:

• Design and implement scalable data ingestion pipelines.
• Build feature processing and baseline computation systems.
• Productionize models for prediction and detection.
• Develop and operate low-latency service and robust offline workflows.
• Architect horizontally scalable services with clear separation between components, leveraging orchestration for distribution.
• Implement monitoring and feedback loops for continuous model and signal improvement.
• Collaborate with Platform teams to integrate operational signals into monitoring and diagnostics.
• Implement a scalable solution for mitigation and structured analysis.

Who You Are:

• 7+ years of experience in data engineering, distributed systems, MLOps, or infrastructure ML roles in production environments.
• Proven experience building high-throughput streaming or telemetry pipelines (e.g., Kafka, Pulsar, Kinesis, or equivalent).
• Strong experience designing time-series feature pipelines and operating large-scale observability systems.
• Experience building and maintaining feature stores and ensuring offline/online feature parity.
• Hands-on experience deploying ML models to production, including versioning, monitoring, rollback, and drift detection.
• Experience designing scalable microservices deployed in Kubernetes-based environments.
• Strong proficiency in Python and at least one systems language (Go, Rust, or C++).
• Experience working with distributed compute or training systems (e.g., NCCL, PyTorch Distributed, Spark, Ray, Slurm).
• Familiarity with GPU telemetry systems such as NVML or DCGM and hardware-level monitoring concepts.
• Demonstrated experience scaling systems from Proof-of-Concept to production-grade, fleet-level deployments.

Preferred: 

• Experience working on GPU fleet management, hyperscale infrastructure, or AI training clusters.
• Experience building anomaly detection or failure prediction systems for hardware or distributed systems.
• Experience implementing distributed straggler detection or collective-level performance analysis systems.
• Experience developing agentic or LLM-powered reasoning systems for diagnostics or operational intelligence.
• Background in reliability engineering or SRE practices.

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 systems that turn raw infrastructure telemetry into actionable intelligence.
• You’re curious about distributed systems failure modes, GPU performance pathologies, and reliability engineering at scale.
• You’re excited by the idea of moving from anomaly detection to prediction to autonomous root cause reasoning.
• You enjoy designing platforms that protect uptime, revenue, and customer trust through proactive systems thinking.

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! 

What You’ll Do:

The Monolith AI Platform Engineering Team at CoreWeave is responsible for building and scaling the data and workflow backbone that powers the world’s most advanced engineering simulation and AI workflows — our ambition is to become the super‑intelligent AI test lab for the engineering industry, helping customers ship science, faster. From high‑throughput data ingestion and feature pipelines to model training and real‑time inference, our platform delivers the performant, reliable, and trustworthy data foundation trusted by the world’s largest engineering companies.

The Staff Data Engineer will own and evolve Monolith’s platform data services and ETL offerings — the data onboarding, preparation, and lineage capabilities that turn fragmented, real‑world engineering data into production‑ready training and inference pipelines. You’ll partner with Product, Engineering, and Customer‑facing teams to deeply understand client data challenges and translate them into scalable, self‑serve data platform features.

About the Role:

We’re seeking a Staff Data Engineer who can own Monolith’s data platform surface end‑to‑end: from offline batch pipelines and large historical backfills to low‑latency, real‑time streaming data flows that power online inference and feedback loops. You’ll define and drive our data architecture, champion data quality and lineage, and decide how customer data moves through Monolith from raw ingestion to governed, observable, and reproducible training sets.

You’ll primarily work with internal teams (Product, Customer Success, Forward‑Deployed Engineers, Software Engineers, Data Scientists), and step in as a domain expert when clients need deeper guidance.

In this role, you will:

• Own Monolith’s Data Platform & ETL Surface
• Lead the architecture and evolution of core data services for ingestion, transformation, validation, and lineage across training and inference workloads.
• Design and maintain end‑to‑end data models and schemas that make complex engineering, simulation, and telemetry data discoverable, reusable, and performant.
• Define standards, contracts, and APIs for how product teams and integrations interact with data services.
• Design & Operate Batch + Streaming Pipelines
• Build and operate batch pipelines for large‑scale historical imports, retraining data sets, and migrations from legacy environments.
• Design and implement streaming pipelines (e.g., using Kafka or similar technologies) for event‑driven or real‑time ingestion and transformation that support online inference, monitoring, and feedback loops.
• Select and integrate off‑the‑shelf, industry‑proven ETL / ELT technologies (e.g., workflow orchestrators, ingestion frameworks, transformation engines) and own their rollout and long‑term operation.
• Champion Data Lineage, Governance & DataOps
• Implement and maintain end‑to‑end data lineage from source systems to derived features and model artifacts, enabling reproducibility, compliance, and faster debugging.
• Establish DataOps practices: CI/CD for pipelines, observability (metrics, logs, traces), and operational runbooks for data incidents.
• Help define data quality and governance standards in partnership with Security, Compliance, and Customer Success, including support for privacy and regulatory needs (e.g., GDPR‑aligned flows and data residency discussions).
• Partner Across Monolith & CoreWeave
• Work with Monolith product and engineering teams (platform, agents, experiments, reliability) to expose data services that unlock new user workflows and AI capabilities.
• Collaborate with CoreWeave infrastructure and AI platform teams to leverage storage, compute, and observability for reliable data flows.
• Serve as a technical escalation point for forward‑deployed and customer‑facing engineers when questions go deeper than their playbooks (e.g., architecture diagrams of data flow, lineage, and governance constraints).

Who You Are:

• Experience & Level
• Typically 8+ years of experience as a Data Engineer / Data Platform Engineer (or similar), including ownership of production data pipelines and architectural decisions.
• Demonstrated Staff‑level impact: leading critical data domains and cross‑team initiatives.
• Data Engineering & Architecture
• Deep experience designing end‑to‑end data architectures that cover ingestion, storage, transformation, serving, and observability.
• Strong, hands‑on experience with both batch and streaming pipelines:
• Batch: scheduled workloads, historical backfills, retraining pipelines.
• Streaming: Kafka or similar event streaming platforms plus real‑time transformation patterns for low‑latency consumption.
• Proficiency with SQL and at least one major analytical database or data warehouse (e.g., PostgreSQL or similar) including schema design and performance tuning
• Proficiency with Spark, Ray or similar distributed data processing frameworks.
• Solid understanding of data modeling (event logs, star schemas, feature tables) in multi‑tenant SaaS or platform contexts.
• Tooling & Ecosystem
• Hands‑on with data orchestration and ETL tooling (e.g., Airflow, dbt, Dagster, Temporal, or equivalents) and able to evaluate and recommend tools that fit our needs.
• Experience integrating and operating off‑the‑shelf data infrastructure (not just bespoke systems), including rollout, migration plans, and ongoing ownership.
• Familiarity with cloud infrastructure and containerization (Docker, Kubernetes, at least one major cloud provider) for deploying and scaling data workloads.
• Data Lineage, Quality & DataOps
• Extensive experience implementing data lineage solutions and using them for debugging, compliance, and auditability.
• Strong background in data quality: validation frameworks, monitoring, and guardrails that prevent bad data from reaching downstream consumers.
• Proficiency with DevOps / DataOps practices: infra‑as‑code, CI/CD for pipelines, runbooks, and participation in on‑call / incident response for data issues.
• Programming, Systems & Communication
• Strong programming skills in Python for building data services, transformations, and platform integrations, with an emphasis on maintainability and tests.
• Comfortable working in service‑oriented architectures, reasoning about data contracts, SLAs, and failure modes across services.
• Clear written and verbal communicator who can:
• Explain data architectures and trade‑offs to internal stakeholders, and
• Occasionally join client conversations as a deep domain expert to answer questions about data flows, security, and governance (e.g., with CIOs, heads of data, or security counterparts), while day‑to‑day client interaction is handled by forward‑deployed teams.

Preferred:

• Experience in ML/AI platforms or MLOps environments where data pipelines feed experimentation, training, and inference workflows at scale.
• Background with time‑series, simulation, or experimental data (e.g., physical test benches, sensors, engineering simulations).
• Familiarity with feature stores, experiment tracking systems, or model registries and their integration with upstream pipelines.
• Experience designing data systems for regulated or safety‑critical domains, including privacy, residency, and retention considerations.

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. If some of this describes you, we’d love to talk:

• Product‑minded – you design data platforms and pipelines that are intuitive to use and unlock real workflows for engineers and customers.
• Systems thinker – you enjoy mapping complex data flows, understanding failure modes, and building robust, observable systems.
• Pragmatic – you balance solid engineering with shipping value quickly, knowing when to invest in abstractions and when to deliver incremental improvements.
• Collaborative owner – you partner well across engineering, product, and customer‑facing teams, and you feel responsible for outcomes, not just code.

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, and foster an environment that encourages collaboration and 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!

What You’ll Do:

The Monolith AI Platform Engineering Team at CoreWeave is responsible for building and scaling the data and workflow backbone that powers the world’s most advanced engineering simulation and AI workflows — our ambition is to become the super‑intelligent AI test lab for the engineering industry, helping customers ship science, faster. From high‑throughput data ingestion and feature pipelines to model training and real‑time inference, our platform delivers the performant, reliable, and trustworthy data foundation trusted by the world’s largest engineering companies.

The Senior DataOps Engineer II will own and drive all things data observability and operations across our client estate — building the practices, tooling, and culture that make Monolith’s data flows debuggable, auditable, and safe to evolve. You’ll sit at the intersection of platform engineering, data engineering, and reliability, implementing end‑to‑end lineage and DataOps practices while mentoring data producers and consumers on how to manage data as a first‑class product.

You’ll partner closely with Monolith’s Product, Engineering and forward‑deployed teams, as well as with CoreWeave’s infrastructure and AI platform groups, to turn fragmented, real‑world engineering data into well‑governed, observable, and operationally robust pipelines powering our SaaS platform and client‑specific deployments.

About the Role:

We’re seeking an Senior DataOps Engineer II who can act as the hands‑on owner for Monolith’s data observability and operational surface: from batch and streaming pipelines running on our platform, through to the lineage, quality, and runbooks that keep customer environments healthy.

You’ll define and roll out DataOps practices (CI/CD, infra‑as‑code, data SLOs, incident response) across the Monolith estate, implement end‑to‑end data lineage and observability, and serve as the go‑to mentor for engineering teams and client‑facing colleagues on best‑practice data management.

In this role, you will:

• Own Monolith’s Data Observability & Operations Surface
• Design and implement the end‑to‑end observability stack for data workloads (metrics, logs, traces, and data‑quality signals) across batch and streaming pipelines.
• Define and maintain operational SLOs/SLAs for critical data flows powering training, inference, and analytics, and ensure they are measurable and actionable.
• Build dashboards, alerts, and runbooks that allow engineers and on‑call responders to quickly detect, triage, and remediate data incidents.
• Standardise “golden paths” for how teams instrument pipelines, expose health signals, and respond to data‑related failures.

• Implement Data Lineage, Quality & Governance
• Deploy and maintain end‑to‑end data lineage for key domains — from client sources through transformations to features, models, and downstream analytics  so teams can debug, audit, and reason about change.
• Define and roll out data quality checks (schema, freshness, completeness, distribution, drift) and ensure failures integrate cleanly into alerting and incident workflows.
• Partner with Security, Compliance, and customer‑facing teams to encode data governance requirements (e.g., retention, residency, access controls) into our pipelines and tooling.
• Help shape metadata models and catalog conventions so that producers and consumers can reliably discover, understand, and use shared datasets.

• Enable DataOps Practices Across Teams
• Establish CI/CD patterns for data pipelines and related infrastructure, including testing strategies, promotion workflows, and change‑management guardrails.
• Drive adoption of infra‑as‑code for data infrastructure (e.g., pipeline orchestration, storage, observability components), reducing manual drift across environments.
• Define and continuously improve DataOps processes — incident response, post‑incident review, change review, on‑call rotations — with a focus on learning rather than blame.
• Evaluate and integrate best‑of‑breed DataOps and observability tooling where it accelerates our teams, balancing build vs. buy pragmatically.

• Partner Across Monolith, CoreWeave & Clients
• Work with Monolith platform, data, agent, and reliability teams to expose observability and lineage as shared services and patterns other engineers can build on.
• Collaborate with CoreWeave infrastructure and AI platform teams to leverage underlying storage, compute, networking, and observability in service of robust data flows.
• Serve as a technical escalation point for forward‑deployed and customer‑facing engineers when data issues cross service boundaries or require deeper architectural insight.
• Mentor data producers (product teams, integrations, forward‑deployed engineers) and data consumers (data scientists, analysts, client engineers) on resilient schemas, contracts, and operational practices.

Who You Are:

• Experience & Level
• Typically 5–6+ years of experience in DataOps, Data Engineering, DevOps/SRE for data platforms, or similar roles, including end‑to‑end ownership of production data pipelines and their operations.
• Proven track record of operating at Senior IC scope: leading cross‑team initiatives, introducing new practices/tooling, and improving reliability at the platform level.

• DataOps, Pipelines & Tooling
• Strong hands‑on experience designing, deploying, and operating data pipelines in production (batch and/or streaming), including failure modes, retries, and backfills.
• Practical experience with data orchestration and ETL/ELT tooling (e.g., Airflow, Dagster, dbt, Temporal, or similar) and comfort evaluating and integrating new tools where appropriate.
• Solid SQL and/or Spark skills and experience with at least one major analytical database or warehouse; familiarity with time‑series / telemetry data is a plus.

• Observability, Lineage & Data Quality
• Extensive experience implementing data observability — metrics, logging, tracing, dashboards, and alerting — for data‑centric workloads.
• Hands‑on work with data quality frameworks and/or observability platforms to monitor freshness, completeness, schema changes, and anomalies.
• Experience deploying and using data lineage or metadata/catalog solutions, and applying them to debugging, compliance, and change‑impact analysis.

• Platform, Infrastructure & Automation
• Comfortable working in containerised, cloud‑native environments (Kubernetes plus at least one major cloud provider); experience with GPU‑ or compute‑intensive workloads is a bonus.
• Strong automation mindset: infra‑as‑code, CI/CD, and configuration management for data infrastructure and observability components.
• Proficient in Python for building tooling, pipeline glue, and platform integrations; additional languages are a plus.

• Collaboration, Mentorship & Communication
• Clear communicator who can explain complex data flows and failure modes to both deeply technical and non‑specialist audiences.
• Experience mentoring engineers and data practitioners on better data management, observability, and operational hygiene — through documentation, examples, reviews, and office hours.
• Comfortable working in a fast‑moving, high‑ambiguity environment where we balance rapid iteration with the safety and reliability demanded by enterprise engineering clients.

Preferred:

• Experience in ML/AI platforms or MLOps environments where data pipelines power experimentation, training, and inference at scale.
• Background with test, simulation, or time‑series data (e.g., physical test benches, battery labs, automotive/aerospace R&D).
• Familiarity with feature stores, experiment tracking, or model registries and their interaction with upstream data pipelines.
• Prior work in multi‑tenant SaaS platforms, especially those with strong compliance, observability, and uptime requirements.
• Experience supporting or partnering closely with forward‑deployed / professional services teams in complex customer environments.

Wondering if you’re a good fit?

We believe in investing in our people, and value candidates who bring diverse experiences — even if you don’t tick every single box. Here are a few qualities we’ve found compatible with our team. If some of this sounds like you, we’d love to talk:

• Data‑obsessed operator – You care deeply about making data systems observable, predictable, and easy to reason about, not just “working most of the time.”
• Systems thinker – You enjoy mapping complex data flows across services, understanding failure modes, and designing for graceful degradation and rapid recovery.
• Pragmatic – You know when to build the ideal abstraction and when to ship the smallest change that meaningfully reduces risk or toil.
• Collaborative mentor – You get energy from helping other teams level up their data practices, and you can influence without heavy process or authority.
• Owner’s mindset – You feel responsible for the outcomes of the platform as a whole, not just the code you write, and you follow issues across boundaries until they’re truly resolved.

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, and foster an environment that encourages collaboration and 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!

What You’ll Do:

The Monolith AI Engineering Team at CoreWeave is responsible for building and scaling the core platform that powers the world’s most advanced engineering simulation and AI workflows. From evolving distributed architecture to enabling the next generation of agentic applications and physical AI, this team delivers the performant, reliable, and scalable foundation trusted by the world’s largest engineering companies.

About the role:

We’re seeking a Principal Software Engineer to lead the architectural evolution of our platform as we scale to meet growing demand from the world’s largest engineering companies. This is a hands-on technical leadership role where you’ll drive critical architectural initiatives while mentoring our engineering team. You’ll own — in conjunction with the VP of Engineering — the technical vision and execution of our platform’s evolution to a more distributed architecture, making it agentic ready for the next generation of AI applications.

In this role, you will:

• Technical Leadership & Architecture
• Drive the ongoing transition from monolithic to distributed architecture.
• Design and implement architecture to be agentic ready, enabling rapid deployment of AI products and MCP server integrations.
• Own technical decisions balancing immediate business needs with long-term architectural goals.
• Hands-On Development
• Write production-quality Python code for critical platform components.
• Build and optimize distributed compute services.
• Implement reliable workflow orchestration patterns.
• Team Mentorship & Collaboration
• Mentor mid-level developers, elevating the technical capabilities of the entire team.
• Establish engineering best practices and architectural patterns.
• Collaborate with product and business stakeholders to translate requirements into technical solutions.
• Guide technical decision-making and prioritization across the engineering organization.
• Interview and onboard new engineers.

Who You Are:

• 12+ years of software engineering experience with at least 2 years in principal/staff engineering roles.
• Expert-level Python development skills with production experience at scale.
• Deep expertise in distributed systems architecture.
• Strong Kubernetes experience with production workloads.
• Hands-on experience with workflow orchestration tools (Temporal, Airflow, Kubeflow, or similar).
• Cloud platform expertise (AWS required, Azure & GCP beneficial).
• Experience with data processing frameworks (Spark, Athena, Ray, or similar).
• Experience with systems having ML Engineering and MLOps aspects.
• Proven track record of leading architectural transformations in growing companies.
• Excellence in technical communication and stakeholder management.

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.

• Technical Excellence: You write clean, maintainable code and design systems that scale.
• Pragmatic Problem-Solver: You balance perfectionism with shipping, making smart trade-offs between short-term delivery and long-term goals.
• Strong Leadership: You proactively identify problems and drive solutions forward.
• Mentorship Mindset: You elevate others through knowledge sharing and patient guidance.
• Ownership Mentality: You take full responsibility for outcomes and drive initiatives to completion.
• Customer Obsession: You understand that our success is measured by our customers’ success.

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! 

What You’ll Do:

The Monolith AI Engineering Team at CoreWeave is responsible for building and scaling the core platform that powers the world’s most advanced engineering simulation and AI workflows — our ambition is to become the super‑intelligent AI test lab for the engineering industry, helping customers “ship science, faster.” From evolving distributed architecture to enabling the next generation of agentic interfaces and physical AI, this team delivers the performant, reliable, and scalable foundation trusted by the world’s largest engineering companies.

The Fullstack Engineer (Frontend Focus) will own and evolve the Monolith platform’s product surface — the data‑heavy, workflow‑centric web experience where engineers design experiments, compare runs, interact with agents, and turn complex physical test data into decisions. You’ll partner closely with Design, Product, and Backend Engineering to ship UI and UX that feel fast, intuitive, and trustworthy for non‑ML expert engineers, while also contributing to our Python backend services when needed.

About the Role:

We’re targeting an IC5 Fullstack Staff Engineer with a strong frontend background, and are also open to exceptional Senior Fullstack Engineer II (IC4) candidates who can operate at staff‑like scope. The role is heavily frontend‑weighted (roughly 80–90%), focused on owning the Monolith web application: component architecture, design system evolution, performance, and UX for complex, data‑rich workflows. You’ll also work across our Python services (FastAPI/Flask) to shape and integrate APIs, bring new platform capabilities to the UI, and occasionally deliver backend features that unlock better user experiences.

You’ll be a key partner in defining how engineers experience Monolith’s “AI test lab” — from agent chat to experiment exploration and monitoring — and will help set the bar for frontend quality, reliability, and developer experience across the team.

In this role, you will:

Frontend Ownership & Experience

• Own significant areas of the Monolith frontend, from architecture and component design to implementation, refactors, and ongoing maintenance.
• Design and build modern, responsive UIs using React and TypeScript, with a focus on complex, data‑heavy views (tables, charts, experiment timelines, dashboards, agents) that remain fast and understandable for users.
• Collaborate with Design to evolve our design system and component library, ensuring consistency, accessibility, and a high‑quality visual language across the product.
• Drive frontend performance improvements (bundle size, rendering strategies, network usage) to keep the app responsive even under large datasets and long‑running workflows.
• Champion accessibility and usability, ensuring the interface works well for time‑pressed engineers in demanding environments.

Fullstack Development & Platform Integration

• Work closely with backend engineers on our Python services to design clean API contracts, shape data models for UI needs, and debug issues across the stack.
• Implement and integrate backend functionality (primarily in Python) when it materially improves UX or unlocks new workflows, including occasional work on API endpoints, background tasks, and data transformations.
• Collaborate with our agent and platform teams to expose agentic capabilities (e.g. platform agent) safely and intuitively in the frontend experience.
• Contribute to fullstack observability — instrumenting both frontend and backend for metrics, logging, and tracing, and using that data to guide product and performance improvements.

Collaboration, Quality & Developer Experience

• Work hand‑in‑hand with Product and Design to scope projects, define success criteria, and break down ambiguous problems into iterative deliverables.
• Maintain a high bar for code quality through thoughtful design, code reviews, tests (unit, integration, and E2E), and documentation.
• Help improve our frontend developer experience — build tooling, workflows, and patterns that make it easier for the team to ship reliable UI quickly (Storybook/design‑system improvements, local dev ergonomics, CI checks, etc.).
• Mentor other engineers on frontend best practices, UX thinking, and effective fullstack collaboration.

Who You Are:

Experience & Level

• Typically 7+ years of professional software engineering experience, including substantial time building and maintaining complex, user‑facing web applications as a fullstack or frontend‑leaning engineer.
• A track record of operating at Senior/Staff scope: owning significant product surface area, driving cross‑team projects, and making architectural decisions that last.

Frontend Expertise

• Advanced proficiency in React and TypeScript, with experience structuring large‑scale frontends (modules, state management, routing, data‑fetching patterns).
• Strong command of web fundamentals
• Experience building data‑heavy and visualization‑centric UIs — e.g. dashboards, charts, time‑series visualizations, experiment or metrics explorers.
• Familiarity with design systems and component libraries (Storybook or similar), and an eye for when design details materially impact user trust and productivity.

Backend & Platform Skills

• Solid experience with Python in production environments (FastAPI, Flask, or Django), including building and integrating REST or GraphQL APIs that power frontend experiences.
• Comfortable working in service‑oriented or microservice architectures, reasoning about data flows, failure modes, and performance across boundaries.
• Experience with SQL databases (e.g. PostgreSQL) and practical data modelling for product features.
• Familiarity with modern cloud and container stacks (Docker, Kubernetes, and at least one major cloud provider).

Quality & Ways of Working

• Strong habits around testing (unit, integration, and E2E) and CI/CD for frontend and backend changes.
• Effective collaborator with Product, Design, and cross‑functional stakeholders; clear written and verbal communicator who can explain trade‑offs and constraints.
• Comfortable working in a fast‑moving, high‑ambiguity environment where we balance shipping quickly with maintaining a reliable platform.

Preferred:

• Experience building UIs for technical or data‑savvy users (ML engineers, data scientists, physical test engineers, or similar), especially in B2B SaaS products.
• Familiarity with GraphQL and typed API clients in React (or strong REST plus a desire to pick up GraphQL quickly).
• Experience with SSR/SSG frameworks, particularly for performance‑sensitive dashboards.
• Prior work with time‑series and experiment tracking tooling, ML platforms, or analytics products.
• Exposure to ML/AI workflows (experiments, training runs, evaluations, agents) and interest in “tools for AI engineers” as a product domain.
• Experience with frontend observability (Sentry, Datadog, OpenTelemetry, etc.) and using telemetry to guide UX improvements.

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.

• Product‑minded craftsperson – you care as much about the user experience and clarity of a workflow as you do about the quality of the code behind it.
• Detail‑oriented on complex UX – you enjoy making intricate, data‑heavy interfaces feel simple and intuitive, and you’re willing to sweat the small interactions to get there.
• Pragmatic and velocity‑driven – you know when to build the perfect abstraction and when to ship a well‑engineered, incremental improvement that moves the product forward.
• Collaborative and empathetic – you build strong relationships with engineers, designers, and PMs, and you’re comfortable iterating based on feedback from customers and colleagues.
• Owner’s mindset – you feel responsible for the experience end‑to‑end, follow through on problems across the stack, and care deeply about the impact your work has on customers and the business.

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!