We’re looking for a Principal Engineer to drive the technical direction and architecture of Platform-as-a-Service (PaaS) at DigitalOcean. Our mission is to make deploying, scaling, and managing applications as simple and seamless as possible for developers around the world—from first deploy to internet scale.

As a Principal Engineer on the PaaS team, you’ll play a key role in designing and building the next generation of cloud-native services such as App Platform, Functions, and CI/CD. You’ll architect systems that power our global infrastructure, partner closely with engineering and product teams across the company, and mentor engineers to level up the entire organization. This is a unique opportunity to shape how developers build and ship software on a platform purpose-built for simplicity and scale.

We’re looking for someone who can span technical strategy and hands-on execution—someone who thrives on solving complex distributed problems and multiplying the impact of everyone around them.

What You’ll Do:

• Architect and guide the implementation of high-scale, reliable, and secure systems that underpin our PaaS offerings.
• Drive technical direction and make foundational decisions on system design, scalability, observability, and developer experience.
• Partner with product managers and engineering leadership to define short- and long-term roadmap priorities.
• Identify platform bottlenecks, inefficiencies, and future risks—and design solutions that proactively address them.
• Collaborate across teams to ensure seamless integration between platform services, infrastructure, and developer tools.
• Mentor engineers at all levels and contribute to a culture of technical excellence, inclusivity, and impact.
• Stay ahead of emerging technologies, patterns, and best practices in the PaaS, serverless, and cloud-native ecosystem.

Key Metrics:

• Platform availability and system performance
• Developer adoption and satisfaction
• Technical debt reduction and architectural clarity
• Engineering velocity and cross-team alignment

What You’ll Add to DigitalOcean:

• 11+ years of experience designing and building distributed systems at scale.
• Expertise in one or more backend languages (e.g., Go, Python, C++, Java) and strong fundamentals in systems architecture and infrastructure.
• Proven experience leading the design of large-scale services used by 1000s (or millions) of customers.
• Passion for mentoring and elevating those around you—technical leadership is about multiplying impact.

Compensation Range:

• $227,040 - $283,800

*This is a remote role

JR: 2025-7225

#LI-Remote

#LI-SK1 

We are looking for a Staff Forward Deployed Engineer who is passionate about solving complex cloud infrastructure challenges in the fast-growing AI/ML space. This is a high-impact position designed to be the "technical tip of the spear" for our most strategic AI-Native (ANE) customers.

As an FDE, you will sit at the intersection of Product Engineering and Customer Implementation. You will be embedded directly with our most strategic customers to drive transformational AI adoption. You will collaborate closely with customer teams to ship advanced AI applications that solve real world business problems.

You will build the tools, migration scripts, and AI starter kits that don't just solve one customer's problem, but scale the entire DigitalOcean AI Cloud ecosystem. Your mission is to accelerate "time-to-inference" in production at scale and serve as a critical feedback loop, ensuring our product roadmap is informed by the world's most demanding AI workloads.

What you’ll do:

• Embedded Customer Engineering: Partner deeply with strategic ANEs to manage complex migrations, build production-ready PoCs, and execute hands-on application builds on our GPU infrastructure.
• Build Scalable Assets & Tooling: Operationalize field learning by building migration planners, reusable systems, benchmarking frameworks, model optimization agents, and deployment automation scripts (e.g., Terraform/Pulumi) that standardize how AI workloads are deployed and tuned across DigitalOcean.
• Develop AI "Starter Kits": Create pre-built GenAI agents, solution templates, and notebooks (e.g., LangGraph, CrewAI) tailored to popular and advanced business needs.
• Drive the Product Roadmap: Act as the frontline technical voice for CPTO, surfacing architectural gaps and edge cases to inform core product development. Transition field-built tools into native product features.
• New Tech Validation: Lead the early testing and integration of emerging AI frameworks to ensure a first-class developer experience on DigitalOcean.
• Partner & GTM Enablement: Co-develop delivery frameworks with Strategic and Technical partners to enable repeatable, high-quality deployments at scale.
• Market Leadership: Produce field-tested demo kits and deployment guides that support new product releases with practical, validated assets.

Key Metrics

• Customer Adoption: Number of high-impact production workloads landed; reduction in average "time-to-production"; and Pilot-to-Production conversion rates.
• Asset & Tooling Delivery: Number of reusable tools (agents, scripts, playbooks) delivered per quarter and their subsequent adoption rate by the crossfunctional teams and customers.
• Field Enablement & Scale: Measured by the successful handoff and execution of FDE-defined deployment patterns to technical partners and the internal adoption rate of FDE playbooks across all customer-facing technical teams.
• Product Influence: Number of roadmap features or architectural changes directly attributed to FDE feedback and validated customer hypotheses.

What You’ll Add to DigitalOcean:

• AI/ML Architecture & Domain Expertise: Significant experience in the AI/ML lifecycle, specifically hosting large language or multimodal models using inference engines like vLLM, SGLang, or Modular. You have a deep understanding of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).
• Distributed Systems & Infrastructure Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton). Expert proficiency in Kubernetes (K8s) and the design of distributed systems, including microservices, messaging systems, databases, and Infrastructure as Code. You can seamlessly integrate AI workloads with core cloud services (Networking, VPC, Storage, Compute).
• High-Performance Inference & Hardware Optimization: Hands-on experience with distributed inference serving frameworks (e.g., llm-d, NVIDIA Dynamo, or Ray Serve). You understand GPU-level optimization and have experience with interconnect technologies like NVlink, XGMI, or RoCE to maximize hardware efficiency.
• Software Engineering Foundation: Strong production coding skills in Python or Go with the ability to build high-quality tools, automation, and internal assets.
• Data-Driven Performance Insight: Proven ability to benchmark AI infrastructure and perform GPU utilization tuning to optimize customer ROI and workload performance.
• Consultative Execution: Proven ability to establish instant technical credibility with CTOs and Lead Architects while managing high-stakes technical migrations.

Preferred

• Experience with emerging AI orchestration frameworks like LangGraph, CrewAI, or LlamaIndex.
• A background in technical consulting or "Forward Deployed" roles at high-growth AI or infrastructure companies.
• Experience building and scaling "Center of Excellence" models or partner delivery frameworks.
• Active contributor to open-source AI projects or technical communities.

Compensation Range: 

• $195,000 - $239,000

*This is a remote role

JR: 2026-7748

#LI-Remote

We want people who are passionate about troubleshooting complex problems with systems, networking and storage at scale.

We are looking for a seasoned system administrator to help us keep the cloud running smoothly. Reporting to the manager of Cloud Operations, the GPU Operations Engineer monitors and provides first-response to all cloud health issues that impact, or could potentially impact, customer experience - internal or external. You will interface with teams across the organization to research and troubleshoot issues from single droplets to cloud-wide disturbances. Our workweek spans five days, and that may involve working on weekends.

What You'll Be Doing: 

• Ensuring maximum uptime for our global infrastructure
• Automating processes and building tools to improve operational efficiency
• Coordinating operational work across teams to improve the platform with minimal impact

What You'll Add to DigitalOcean:

• Solid experience with Linux operating systems or Networking and day to day upkeep
• Familiarity with virtualization technologies and troubleshooting virtual machine instances
• Familiarity with containerization technologies and troubleshooting containers
• Familiarity with IPv4 Networking and troubleshooting (CCNA equivalent)
• Basic storage concepts and technologies
• Experience with monitoring systems and incident management
• Experience scripting in one or more of the following languages: Bash, Python, or Go
• Experience with GPU hardware or AI/ML, and Kubernetes
• A passion for good documentation and open communication
• Proven ability to learn!

Compensation Range: 

• $107,000 - $134,000

*This is a remote role

JR: 2026-7423

#LI-Remote

About the Position
At 6sense, the Post-Sales Solutions Architect is a critical partner to our Strategic and Enterprise Customer Success Management teams, focused exclusively on supporting existing customers throughout their journey with us. This role blends deep technical acumen with strong customer engagement skills to lead architecture design, optimize platform adoption, and ensure our customers realize the full value of their investment.

You'll work directly with our largest and most complex accounts, helping them integrate and scale their use of 6sense by designing innovative solutions, advising on best practices, and serving as a trusted technical advisor post-sale. This is a high-impact, customer-facing role designed to drive retention, value delivery, and long-term growth.

Key Responsibilities

• Partner with Strategic and Enterprise CSMs to lead post-sale technical activities, including product enablement, demos, proof-of-value workshops, and advanced use case development.
• Facilitate architecture design sessions and platform integration planning, aligning customer objectives to 6sense solutions.
• Document business and technical requirements, and clearly communicate the business value of 6sense capabilities to technical and executive stakeholders.
• Serve as a technical liaison between Customer Success, Product, and Delivery teams to ensure a seamless customer experience.
• Lead solution design for multi-product implementations, systems integrations, and data architecture within existing customer environments.
• Proactively identify technical optimization opportunities to improve customer outcomes and platform ROI.
• Contribute feedback to product management and engineering teams, and help evolve internal tooling and processes for customer delivery.
• Build reports and dashboards that help customers track progress, optimize performance, and make data-driven decisions.

Requirements

• 5+ years of experience in a customer-facing technical role, such as Solutions Architect, Technical Account Manager, or Pre/Post-Sales Engineer.
• Deep technical expertise in solution architecture, data integration, and enterprise software.
• Strong interpersonal and communication skills; able to build trust and articulate complex solutions to both technical and business audiences.
• Experience with CRM systems such as Salesforce, Microsoft Dynamics, or SAP.
• Familiarity with marketing automation tools (Marketo, Eloqua, Pardot, Hubspot) and Customer Data Platforms (Adobe Experience Platform, Segment, etc.).
• Experience with ETL/ELT tools (Informatica, DataStage, Netezza) and data visualization tools (Tableau, Looker, Power BI).
• Working knowledge of cloud infrastructure (AWS, Azure, GCP), REST/SOAP APIs, and integration platforms (MuleSoft, Zapier, Dell Boomi).
• Proficiency in programming/scripting languages such as Python, Java, or C++.

Nice to Haves

• Strong problem-solving skills with a customer-first mindset.
• A track record of influencing customer outcomes through consultative engagement.
• Experience supporting SaaS adoption, expansion, or renewal in large-scale enterprise accounts.
• Ability to prioritize and execute across multiple customer projects while managing stakeholder expectations.

Base Salary Range: $139,035.00 - $180,918.00. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO). #Li-remote

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Senior Software Engineer, Energy Storage

This position is on a team building the software platform that powers optimized control and market participation of battery energy storage systems (BESS) — software that runs both on site controllers and in the cloud. Our scope spans the full stack: compute infrastructure, telemetry, asset modeling, alerting, feature ingestion and storage for forecasting and optimization, simulation and operational control orchestration, and integration with energy markets. We're a lean team that holds ourselves to high standards in both the quality of what we build and the speed at which we build it. We value collaboration, trust, learning, and engineering excellence, and we genuinely enjoy building great things together.

Responsibilities will include:

• Contribute to BESS data engineering — building and maintaining telemetry pipelines and stores, and feature ingestion and storage systems for forecasting and optimization
• Design, build, and maintain simulation and training infrastructure — platforms for running and orchestrating simulations at scale
• Develop and operate real-time forecast and optimization workflows, orchestrating pipelines that drive BESS operations
• Support energy market integration, including data ingestion and bid management
• Collaborate across the full stack and across team boundaries to ensure the product system works well as a whole
• Write well-tested, well-monitored software and iteratively improve it over time
• Apply AI tools to accelerate development while maintaining high system design quality and code standards

Desired Qualifications: 

• Strong software design instincts — thinks in systems, reasons from first principles, and understands the real-world challenges of building available, reliable, scalable, and secure distributed systems
• Comfortable using AI tools to move faster without letting them erode system design or code quality
• Excellent collaborator — communicates clearly, listens well, and makes the people around them better
• Bias toward action — takes initiative, makes progress in ambiguous situations, and doesn't wait for perfect information
• Systems thinker and owner — willing to think and collaborate across team boundaries to make sure the product works well as a whole
• Rigorous about quality — writes well-tested, well-monitored software and iterates on it over time
• B.S. or advanced degree in Software Engineering, Computer Science, or another discipline, combined with 2-5 years of experience in software engineering.
• Experience with relevant technologies a plus: Kubernetes, Rust, Python, NATS, PostgreSQL/TimescaleDB, SQLite
• A passion for sustainability and making the world a better place! 

Software Validation Engineer, Energy Storage 

The Redwood Vision: Architecting the Future of the Grid 

We are at the precipice of a global energy transition. At Redwood Materials, we are moving beyond battery recycling to build the most advanced, circular energy ecosystem on the planet. Our goal is to shift the industry from static, hardware-heavy infrastructure toward agile, software-defined energy assets. The Software Validation team ensures that these complex systems are safe, resilient, and ready for grid-scale deployment. 

As a Software Validation Engineer, you are the bridge between pure code and physical reality. You will design and maintain the environments—Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL)—that allow us to stress-test our power electronics and site controllers. Your mission is to build the automated frameworks that catch edge cases before they hit the field, ensuring every product we ship meets the highest standards of reliability. 

Key Responsibilities 

• Validation Environment Design: Architect and scale our SIL and HIL environments (using platforms like Typhoon HIL, Opal-RT, or similar) to provide high-fidelity simulations of battery assets and grid conditions
• Test Automation Frameworks: Lead the development of automated test suites in Python. You will transition our validation processes from manual intervention to a fully automated, scalable framework
• V&V Strategy: Define and execute comprehensive Verification and Validation (V&V) plans, ensuring all software requirements for the site controller and embedded power electronics are met under normal and fault conditions
• CI/CD Integration: Integrate automated HIL/SIL testing into our CI/CD pipelines, providing real-time feedback to the development teams and serving as the final gate for production-ready code
• Hardware-in-the-Loop Interfacing: Design custom interface hardware and wiring for HIL benches, bridging physical I/O from our controllers to simulated power stages
• Root Cause Analysis: Work closely with the firmware and systems teams to debug complex failures found during validation, using logic analyzers, oscilloscopes, and telemetry logs to pinpoint issues

Qualifications 

• Background: A BS or MS in Electrical Engineering, Computer Engineering, or a related field, combined with 2+ years of professional experience in software validation or test automation for hardware-focused systems
• HIL/SIL Mastery: Proven experience with real-time simulation platforms (Typhoon HIL, Opal-RT, or dSPACE) and a deep understanding of how to model power electronics components in a virtual environment
• Automation Toolchain: Expert proficiency in Python (specifically for test automation frameworks like Pytest) and familiarity with Rust and C/C++ to navigate embedded codebases
• Hardware Literacy: Comfortable in a high-voltage lab environment. You should be proficient with standard lab equipment (oscilloscopes, power supplies, electronic loads) and digital protocols like CAN, SPI, and Ethernet
• Systematic Thinking: A dedicated mindset for "breaking" software. You have a track record of finding critical edge cases in complex state machines and control loops
• Infrastructure: Experience with Git, Jenkins/GitHub Actions, and containerization tools like Docker for managing consistent test environments

Operations Program Manager

We are seeking a highly technical Operations Program Manager to lead build readiness & internal ramp execution for hardware manufacturing. This role requires strong hands-on manufacturing engineering instincts, the ability to dive into process capability and equipment readiness, and real experience improving throughput, yield, and reliability at production facilities. You will manage builds from prototypes to mass production, working with cross-functional teams to ensure processes are capable and stable at scale.

Responsibilities will include:

• Drive cross-functional execution from NPI through mass production, aligning Engineering, Manufacturing, Supply Chain, Quality, Finance, and external stakeholders to meet build and ramp targets
• Define ramp success criteria, build plans, and phase-gate reviews to enable smooth transition from proto → pilot → MP → scale
• Manage program schedules, risks, and dependencies; maintain clear dashboards and communication, escalating critical issues with data-driven options for resolution
• Own factory and supplier readiness for PCBA & box builds: lead technical reviews, capacity & CapEx planning, process qualification, line bring-up, and on-site build support
• Drive yield improvement and issue closure by partnering with internal teams and manufacturing sites to validate process, test coverage, and quality systems
• Lead root cause, containment, corrective action, and long-term process improvements
• Establish run-at-rate requirements and validate capacity at suppliers and internal sites
• Identify bottlenecks and implement solutions for cycle-time reduction, debottlenecking, and throughput increase
• Serve as the primary interface between internal product teams and external manufacturing partners to ensure aligned execution and accountability
• Support go-to-market operational readiness, ensuring supply availability, logistics, and sustaining plans are in place for launch and scale

Desired Qualifications:

• 5+ years in program management, CM operations, manufacturing & process engineering, supplier industrialization, or technical operations in high-volume hardware
• Demonstrated success improving yield, throughput, cycle time, and OEE in production
• Hands-on experience with PPAP/APQP, DOE, SPC, PFMEA, GR&R, control plans
• Strong data analysis skills: Excel/Sheets; SQL, JMP, Minitab or Python beneficial
• Ability to read mechanical and electrical drawings
• Experience in automotive or power electronics manufacturing
• Experience scaling hardware production from lab/pilot → volume manufacturing

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 Sr. Research Scientist on the Scaling team, you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. And most importantly, you will love our customers: our goal is to make our customers successful in applying state-of-the-art LLMs and AI systems, and we encode our scientific expertise into our products to make that possible.

The Impact you will have

As a Sr. Research Scientist on the AI Research Team at Databricks, You Will: 

• Define and lead independent research agendas on foundation model efficiency in model training and reinforcement learning, conducting experiments to empirically validate hypotheses and benchmark against state-of-the-art approaches
• Drive algorithmic innovations for large-scale neural network training or inference (e.g., novel optimizers, low-precision techniques, model adaptation methods)
• Optimize ML systems for distributed training, memory efficiency, and compute efficiency through hands-on implementation.

What We Look for

• MS/PhD in Computer Science or related field with strong foundations in machine learning and systems
• Proven ability to write high-quality, efficient code in Python and PyTorch for research implementation and experimentation
• Strong preference for candidates with first-author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency.

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Opportunity

At Zeta Global, identity is at the core of our marketing cloud. The Identity team builds and operates backend services that power the Zeta Identity Graph - collecting, resolving, and maintaining billions of behavioral, demographic, and transactional signals. These services enable people-based marketing at scale, ensure trust and transparency in how identity data is managed, and provide critical APIs and event streams consumed across Zeta’s platform. Our systems support more than 235 million consumer profiles and process billions of events monthly, requiring services that are highly available, low-latency, and built to scale

We are looking for a Lead Software Engineer who brings strength in both backend distributed systems and data engineering. In this role, you’ll design APIs and event-driven services, scale our systems to handle rapidly growing data volumes, and modernize our platform for resilience, transparency, and performance.

You Will:

• Design and build backend services and APIs for identity resolution, graph transparency, and integrations.
• Develop and optimize event-driven pipelines and workflows processing billions of signals.
• Deploy and operate cloud-native services on EKS/Kubernetes, leveraging Helm for repeatable deployments.
• Ensure reliability, observability, and scalability across services and data pipelines.
• Collaborate across engineering, product, and data teams to deliver end-to-end identity solutions.
• Mentor engineers and influence architecture and roadmap

You Have:

• 5+ years of experience in backend and/or data engineering at scale.
• Strong skills in Python, Java, or Scala, with expertise in APIs, distributed systems, and event-driven design.
• Hands-on experience with Kafka (or similar), Spark/Hive/Flink, and workflow schedulers (Airflow/Luigi).
• Proficiency with Kubernetes (EKS preferred) and deployment practices (Helm, service meshes).
• Solid knowledge of SQL, data modelling, and cloud platforms (AWS).
• Familiarity with columnar / NoSQL databases (Aerospike, Snowflake, HBase, Scylla, Couchbase).
• Strong communication skills and a track record of driving technical direction and mentoring teams.

Bonus Skills:

• Experience with large-scale or multi-tenant distributed systems.
• Exposure to real-time streaming frameworks (Flink, Storm).
• Knowledge of open table formats (Iceberg, Hudi, Delta Lake).
• Background in security, compliance, or privacy related to identity systems.

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $150,000 - $200,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-DD1

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Opportunity

At Zeta Global, identity is at the core of our marketing cloud. The Identity team builds and operates backend services that power the Zeta Identity Graph - collecting, resolving, and maintaining billions of behavioral, demographic, and transactional signals. These services enable people-based marketing at scale, ensure trust and transparency in how identity data is managed, and provide critical APIs and event streams consumed across Zeta’s platform. Our systems support more than 235 million consumer profiles and process billions of events monthly, requiring services that are highly available, low-latency, and built to scale

We are looking for a Lead Software Engineer who brings strength in both backend distributed systems and data engineering. In this role, you’ll design APIs and event-driven services, scale our systems to handle rapidly growing data volumes, and modernize our platform for resilience, transparency, and performance.

You Will:

• Design and build backend services and APIs for identity resolution, graph transparency, and integrations.
• Develop and optimize event-driven pipelines and workflows processing billions of signals.
• Deploy and operate cloud-native services on EKS/Kubernetes, leveraging Helm for repeatable deployments.
• Ensure reliability, observability, and scalability across services and data pipelines.
• Collaborate across engineering, product, and data teams to deliver end-to-end identity solutions.
• Mentor engineers and influence architecture and roadmap

You Have:

• 5+ years of experience in backend and/or data engineering at scale.
• Strong skills in Python, Java, or Scala, with expertise in APIs, distributed systems, and event-driven design.
• Hands-on experience with Kafka (or similar), Spark/Hive/Flink, and workflow schedulers (Airflow/Luigi).
• Proficiency with Kubernetes (EKS preferred) and deployment practices (Helm, service meshes).
• Solid knowledge of SQL, data modelling, and cloud platforms (AWS).
• Familiarity with columnar / NoSQL databases (Aerospike, Snowflake, HBase, Scylla, Couchbase).
• Strong communication skills and a track record of driving technical direction and mentoring teams.

Bonus Skills:

• Experience with large-scale or multi-tenant distributed systems.
• Exposure to real-time streaming frameworks (Flink, Storm).
• Knowledge of open table formats (Iceberg, Hudi, Delta Lake).
• Background in security, compliance, or privacy related to identity systems.

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $150,000 - $200,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-DD1

THE ROLE

As an Applied Research Engineer at Snorkel AI, you will own the infrastructure that powers our model training and evaluation work. This is a hands-on role where you will build and operate GPU cluster infrastructure, training pipelines, and the tooling that allows our research and engineering teams to run experiments reliably and at scale. You will work closely with research scientists and engineers, translating training requirements into robust, reproducible systems—and proactively removing infrastructure blockers before they slow down the work that matters most.

Snorkel AI operates in a fast-paced, high-impact environment. We are looking for someone who takes pride in operational excellence, loves solving complex distributed systems problems, and thrives when given real ownership.

Location: Redwood City or San Francisco — OR REMOTE

MAIN RESPONSIBILITIES

• Set up and manage GPU cluster infrastructure on major cloud providers (e.g., AWS HyperPod) for distributed model training, including networking, provisioning, and cost tracking.
• Build and operate job orchestration and scheduling systems (e.g., Kubernetes, Slurm, or cloud-native equivalents) to reliably launch and manage training, rollout, and evaluation jobs across multi-node clusters.
• Integrate and maintain ML training frameworks and post-training pipelines, ensuring they run stably and reproducibly at scale.
• Set up and maintain experiment tracking, dataset versioning, and model artifact management to support fast iteration.
• Monitor and optimize cluster health, inter-node communication, and resource utilization; implement fault tolerance and auto-recovery so long-running jobs survive node failures.
• Work closely with research scientists and ML engineers to understand requirements, unblock experiments, and evolve infrastructure as our training workloads needs change.

PREFERRED QUALIFICATIONS

• Hands-on experience managing GPU clusters on major cloud providers, including provisioning, network configuration, and cost management.
• Experience with distributed compute orchestration tools such as Kubernetes, Slurm, or equivalent cluster management systems.
• Working knowledge of distributed training concepts: parallelism strategies, memory optimization techniques, and inter-node communication.
• Experience with setting up, managing, and integrating ML experiment tracking and data/model versioning tools..
• Strong Python proficiency and solid software engineering fundamentals such as version control, modular design, and automation.
• Ability to work in a fast-moving, iterative environment and take end-to-end ownership of ambiguous infrastructure problems.
• Hands-on experience with post-training workflows such as supervised fine-tuning (SFT) or reinforcement learning (RLHF, GRPO, or similar) is a strong plus, but not required.

This role is a great fit for engineers who love building reliable systems close to the frontier of AI research. We welcome applicants from a wide range of backgrounds—whether your experience comes from industry, research labs, or direct hands-on work with distributed infrastructure at scale.

BE YOUR BEST AT SNORKEL

Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.

Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of 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 law. All employment is decided on the basis of qualifications, performance, merit, and business need.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

About the DevInfra Team

The DevInfra team is Mixpanel’s platform engineering team. Our mission is to be a force multiplier for Mixpanel engineering as a whole. We achieve this by partnering with our engineering teams to build a world-class software development life cycle together.

About the Role

We are looking for an engineer with a strong track record of solving tooling pain points, automating away toil, and helping teams deliver software faster, more safely, and more reliably. Someone who is energized by supporting their fellow engineers. An engineer who has good taste and leverages AI to great effect without generating slop. Frequent forays into uncharted territory requires our team to be highly adaptable and always ready to learn. We serve as trailblazers for the rest of Mixpanel engineering.

Responsibilities

• We partner with a wide variety of teams to build out their software development lifecycle to be optimized for speed, safety, and reliability. We support teams writing front-end UI code as well as teams maintaining our highly stateful storage systems deep in our stack.

• We are responsible for deploying AI tools like Claude Code to our entire engineering team. We are building agents into our platform to do things like augment on-call response and automatically one-shot bugs that come through a team’s triage queue.

• We support systems that ingest more than 1 Trillion user-generated events every month while balancing low end-to-end query latency. Mixpanel queries typically scan more than 1 Quadrillion events over the span of a month. More details in this blog post.

• Mixpanel runs entirely on Google Cloud Platform and Google Kubernetes Engine. DevInfra is the overall admin for our cloud environment, so we wear many hats. We are responsible for things like Terraform, cost management, networking, and security best practices.

• We are the overall service owner for Kubernetes. Individual teams are responsible for maintaining and monitoring their own clusters and workloads. We are responsible for setting standards for deployment, observability, and developer experience. We facilitate efforts like Kubernetes version upgrades and helping teams adopt new Google Kubernetes Engine features.

• Service ownership of our observability pipelines. We have instrumented over 30 million Prometheus metrics time series and use Open Telemetry to ingest over 4 billion distributed tracing spans per month. An example: How We Migrated from StatsD to Prometheus in One Month.

• We maintain our GitHub Actions based CI/CD pipelines. We maintain a robust GitHub-native delivery process that empowers teams to self-serve their needs while ensuring safety and reliability. See this blog post for an example.

• We maintain our devbox system that provisions cloud development environments for individual developers.

• We procure best in breed SaaS tooling for the rest of engineering. We own our relationship with GitHub, Honeycomb, Chronosphere, Sentry, and more.

• We run the new engineer onboarding process. If you join us, your first meeting will probably be with us!

We're Looking For Someone Who Has

• 3+ years of industry experience.

• Experience with at least one of: Go, Python, or JavaScript/TypeScript.

• Production experience working with Kubernetes.

• Production experience managing infrastructure in a major cloud provider such as Google Cloud Platform, Amazon Web Services, or Azure. Preferably Google Cloud Platform.

• Knowledgeable about coding agents like Claude Code

Bonus Points

• Experience with observability solutions like OpenTelemetry, Prometheus, or Distributed Tracing for application monitoring and performance analysis.

• Experience with service mesh.

• You write excellent docs.

• Proficient with GitHub Actions and the GitHub ecosystem.

• Experience building deployment pipelines. We love GitOps!

• Bazel expertise.

• Experience with Terraform.

• Experience with cloud Identity and Access Management (IAM) systems and knowledgeable about security best practices.

• Experience implementing Internal Developer Platforms like Backstage.

• Working knowledge of site reliability engineering (SRE) principles such as implementing Service Level Objectives (SLOs).

• You ❤️ open source.

The Role

Join us in revolutionizing the financial landscape. SoFi is seeking enthusiastic Senior Software Engineers who are ready to lead the development of key advancement to the next generation of our financial services platform.

Data Trust org leads the path for building platform services, and governance initiatives — from integrating diverse data sources into governance tools to evolving the foundational architecture that supports SoFi’s long-term growth

Data Trust’s charter includes

• Development and management of the Unified Data Catalog and Discovery Tools
• Ownership and other metadata associated with the Catalog
• Lineage and retention policies
• Data Quality Strategy and Standardization (Tiered Ownership, SLOs)
• Enforce regulatory compliance and privacy across data
• Data Governance enforcement and tooling (Data Contracts, RBAC mechanisms).
• Building self-service tools for Data Owners and Data Engineers.

What You’ll Do

As an emerging leader within our team, you will focus on building out the backend services for our next-generation metadata architecture. This role involves full product lifecycle engagement, from initial concept to deployment.

Key responsibilities for this role include:

• Implementing new capabilities, ensuring code quality, and delivering highly-available, scalable services.
• Success requires close technical collaboration with Product, Marketing, Operations, Business, Legal, and Compliance teams. You will be responsible for translating business requirements from these stakeholders into effective technical solutions that provide outstanding value to our members.
• Leading the technical evolution of one or more key platform subsystems.
• Expertly leverage existing solution patterns and understand when to differentiate resulting in well-structured, maintainable code.
• Ability to understand data models, APIs, scaling concerns, and failure modes in order to make effective trade-offs between maintainability and performance.
• Creating stability by writing meaningful tests, considering observability, and leveraging your ability to debug complex issues across multiple system layers.
• The ability to create clear design and operational documentation.
• Contributes to code reviews, is a role model for more junior engineers, and contributes to team efficiency, beyond personal output.

What You’ll Need

• Bachelor's or Master's degree in Computer Science, or Software Engineering
• 3+ years as a professional, full-time Software Engineer, or similar relevant work experience.
• Proficient coding in Java, Kotlin, Python or other similar object-oriented programming language.
• Proficient working with GIT and related CI/CD systems.
• Experience with public cloud compute, storage, and infrastructure.
• Experience with any data warehousing solution like Snowflake and IAC tools to provision platform infrastructure.
• Experience with Kafka, Docker, Kubernetes, and Spring Boot or similar frameworks.
• Deep understanding of relational and document database fundamentals.
• Deep understanding of fundamental distributed systems concepts.

Nice To Have

• Full-Stack experience in both back-end (Kafka, Redis, Docker) and front-end (JavaScript, TypeScript, React).
• Experience with microservices design principles and architecture.
• Experience with workflow orchestration technology (AWS step function, Airflow, Temporal).
• Experience working with Data Platforms

We're driven to push boundaries and deliver exceptional products to help our members achieve financial stability. If you have the passion and a commitment to customer success, we want to hear from you.

About the Role

The Data Governance function is pivotal in ensuring the integrity, trustworthiness, and effective management of Chime’s data assets. Our mission is to establish and operationalize data governance frameworks that not only meet compliance requirements, but actively enable high-confidence decision making across the company.  As a Data Governance Engineer, you will develop and implement policies and tools for data quality, developer enablement, certified datasets, and governance automation - with a sharp focus on building trust signals and scorecards that help data consumers quickly understand and act on the reliability of Chime's data.

This is a hands-on engineering role. You will write production-quality code to build automation for governance and data quality processes using Terraform, Python (or similar languages), and contribute to internal libraries, frameworks, and orchestration workflows that enable scalable governance.

You will partner closely with data engineering, analytics, product engineering, and compliance teams to:

• Support Chime in data compliance and risk reduction initiatives
• Establish data quality as a product discipline
• Build trust signals and scorecards that make data reliability transparent and data context valuable for a variety of use cases
• Implement governance workflows upstream, where data are created
• Ensure data context and metadata are sufficient for downstream use cases, including AI applications

You will be involved in reviews of technical designs and implementation plans to provide guidance on appropriate development from a data governance and compliance perspective, acting as a trusted advisor to teams innovating at Chime. You will also be a strong advocate and leader for Artificial Intelligence tooling and adoption at Chime, bringing curiosity and a practical eye for where AI creates leverage in data governance workflows. This role is 40% strategic influence, 40% development and building, and 20% stakeholder management and support.

The base salary for this role and level of experience will begin at $160,000.00 and go up to $221,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Build Data Trust Infrastructure: Design and implement trust scorecards, trust signals, and quality indicators that give data consumers real-time visibility into data reliability - and partner with producers upstream to embed quality checks at the source before issues propagate downstream.
• Drive Data Quality Strategy: Own and evolve Chime's data quality strategy end-to-end. Design and partner with engineering teams to implement data quality frameworks and monitoring systems that proactively identify, surface, and resolve data issues at scale.
• Develop and Implement Data Governance Policies: Create and enforce policies for data classification, quality, and lifecycle management, ensuring data integrity and compliance with SOX and other applicable regulatory standards.
• Enable the Data Catalog: Collaborate on the development, adoption, and enrichment of Chime's data catalog - improving discoverability, context richness (lineage, ownership, definitions, usage guidance), and the overall experience of working with Chime's data.
• Automate Governance Processes: Develop and deploy automation solutions for data governance tasks, such as metadata management, data lineage tracking, access controls, and quality remediation workflows.
• Champion SOX Compliance: Play an active role in Chime's SOX compliance efforts, ensuring that data governance practices, controls, and audit trails meet regulatory requirements and are well-documented.
• Lead AI Adoption for Data Governance: Serve as a leader and advocate for AI tooling within the data governance space - identifying opportunities to apply AI to automate governance workflows, improve data context, enhance trust scoring, and accelerate adoption of governance best practices across engineering teams.
• Collaborate Across Teams: Work closely with data engineers, analysts, and compliance officers to align data governance initiatives with business needs and regulatory requirements.

To thrive in this role, you have

• Experience: 5+ years in data engineering, data governance, or a related field, with hands-on experience in data classification, cataloging, quality assurance, and trust frameworks. Engineering ability to design and implement data governance and quality processes is critical to success.
• Trust & Quality Expertise: Demonstrated experience building data trust signals, quality scorecards, or similar frameworks that make data reliability visible and actionable for both engineers and non-technical stakeholders.
• Programming Experience: Proficiency in Python or a comparable programming language, and experience building scalable data tooling or automation pipelines.
• Curiosity and Relentlessness: You are genuinely curious about data, technology, and the problems that sit at the intersection of the two. You don't let ambiguity stop you - you dig in, ask the right questions, and keep pushing until you've found the right answer or built the right solution. You bring energy and persistence to hard problems and don't need external pressure to drive yourself forward.
• Composure in Complexity: Data governance at a fintech means navigating competing priorities, regulatory constraints, sprawling data ecosystems, and stakeholders with different mental models of "good." You thrive in this environment rather than shrinking from it - you can hold complexity without getting overwhelmed, break it into manageable pieces, and make steady progress without waiting for perfect conditions.
• Exceptional Communication: You are a terrific communicator in both verbal and written form. You can write a crisp one-pager that earns buy-in from a skeptical audience, run a meeting that ends with clarity instead of confusion, and translate deeply technical concepts into plain language that moves people to action. You treat communication as a core part of the job, not an afterthought.
• Product Management Mindset: You think like a product manager as much as an engineer. You ask "who is this for and what problem does it solve?" before writing a line of code. You prioritize ruthlessly, define success before you start building, and care about adoption and outcomes - not just delivery. You treat your internal stakeholders as customers and design solutions with their experience in mind.
• AI Curiosity and Advocacy: Genuine curiosity about AI and its applications in the data governance space, with an appetite for identifying and piloting new tools and workflows that improve the experience of working with Chime's data.
• Technical Proficiency: Strong knowledge of data governance tools and platforms, and experience with cloud data warehouses like Snowflake. Experience with data catalog platforms and driving adoption across engineering organizations is a strong plus.
• Bonus: Compliance Knowledge: Familiarity with SOX, CCPA, and other data privacy and financial regulations, and ability to apply technical skills to governance practices that meet audit and compliance requirements.

#LI-Onsite #LI-GC1

About the Role

The Data engineering team has been created keeping in mind the importance and criticality of data for the overall success of Chime.  The mission of this group is to build scalable data artifacts and insights that enable accurate, governed, data-driven decision-making.. As a core member of the team, you will have the opportunity to build scalable data pipelines and frameworks.  Additionally, you will have the unique opportunity to architect and build workflows that could potentially become de facto standards for the fintech industry.

The base salary offered for this role and level of experience will begin at $164,000.00 and go up to $227,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Be a hands-on data engineer, building, scaling and optimizing ETL pipelines
• Design data schemas and scale them data  for 10x data growth 
• Ownership of all aspects of data - data quality, data governance, schema design, data quality and security
• Develop scalable data architecture and standardized data models that enable self-serve data insights using AI.
• Own the ETL workflows and make sure the pipeline meets data quality and availability requirements
• Work closely with partner teams, like Data Science, Analytics and DevOps

To thrive in this role, you have

• 5+ years experience transforming data to governed and lucid datasets
• 5+ years of hands-on experience to build and deploy production-quality data pipelines
• 5+ years of experience working with stakeholders to provide business insights
• 5+ years of experience writing SQL, Spark, AWS Glue, EMR, Airflow, Python 
• 3+ years of hands-on experience using any MPP database system like Snowflake, AWS Redshift or Teradata
• Track record of successful partnerships with Analytics, Data Science and DevOps teams 
• Understanding of key metrics for data pipelines and has built solutions to provide visibility to partner teams
• Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

#LI-Onsite #LI-GC1

About the role

We’re seeking a Finance Associate to support the Payments function at Chime! In this role, you will play a critical part in ensuring forecasting accuracy while driving forward high-impact strategic and financial initiatives. You’ll partner closely with business leaders and finance team members to provide insights, influence decision-making, and help shape Chime’s financial strategy.

Supporting Cost of Revenue (COR), you will collaborate with cross-functional partners across Strategy, Product, Data, Engineering, and Accounting to drive accuracy, visibility, and predictability in our cost forecasting. You will help design and operationalize scalable forecasting models, improve data pipelines, and ensure alignment between financial models and underlying business drivers.

You will support key COR forecasting areas such as processing, network, and bank-related costs, partnering with stakeholders to understand key drivers like transaction volume, pricing changes, and product evolution. You will also work closely with Accounting during close to validate actuals, explain variances, and ensure financial accuracy.

In addition, you’ll play a key role in the bottoms-up planning and forecasting process,  develop financial models and reporting, and proactively identify opportunities to leverage AI and automation to improve forecasting accuracy, scalability, and efficiency. We are looking for an analytical problem-solver who can break down complex problems into actionable components without losing sight of the overall strategy.

The base salary offered for this role and level of experience will begin at $105,000.00 and up to $145,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Own and improve COR forecasting processes, incorporating key drivers such as transaction volumes, pricing, vendor changes, and product evolution
• Develop, deliver, and maintain Cost of Revenue reporting and dashboards (e.g., vendor costs, transaction drivers) to ensure visibility and accuracy for business and finance leadership
• Partner cross-functionally with Strategy, Product, Data, Engineering, and Accounting to support financial decision-making and accountability
• Support monthly close processes by validating actuals, and explaining variances and business performance across COR categories
• Leverage AI and automation to streamline financial processes, improve data quality, and enhance scalable reporting and forecasting capabilities that support Chime’s growth and enable smarter, faster decision-making
• Support ad hoc analyses and strategic initiatives focused on optimization opportunities across COR

To thrive in this role, you have

• Minimum 3+ years of Finance/FP&A, investment banking, Big 4 accounting/advisory, or consulting experience
• Bachelor's Degree, or equivalent degree in Finance, Accounting, Economics or other relevant field
• Track record of budgeting, forecasting, and financial modeling with strong accuracy and attention to detail
• Strong sense of ownership and commitment to quality, working diligently to navigate ambiguity/obstacles and drive projects to completion
• Strong process-improvement mindset with demonstrated ability to leverage AI and automation to scale finance workflows and improve forecasting precision and turnaround time
• Proven ability to manage multiple priorities and competing deadlines in a fast-paced environment
• Advanced G-Suite, Microsoft Excel, Word, and presentation tools (PowerPoint, G-Slides) skills required
• Experience identifying, designing, or deploying AI-driven use cases within finance workflows (e.g., forecasting automation, reporting acceleration, variance analysis, or scenario modeling)
• Bonus Experience: Pigment, Netsuite, Looker, Workday, SQL, Snowflake, Python/coding tools, and direct experience with R&D and/or transaction loss forecasting

#LI-BE1 #LI-Onsite

About the role

We’re hiring a Lead Data Analyst, Growth Science to use advanced science to estimate the incrementality of Marketing, Product, and Lifecycle Marketing and to improve their performance. As a senior individual contributor, you’ll lead high-impact growth initiatives through the use of media mix modeling, experimentation and quasi-experimentation, and predictive modeling.  You will play a role in defining how we estimate incrementality and how we improve performance.

The base salary offered for this role and level of experience will begin at $152,000.00 and up to $210,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Partner with and support vendor developing of our media mix model
• Design, execute, and read out results of complex experiments and quasi-experiments not supported by our A/B testing vendor
• Develop predictive models to improve performance of Marketing, Product, and Lifecycle Marketing initiatives
• Partner with Marketing, Product, and Lifecycle Marketing, to drive business outcomes
• Mentor other analysts in our team on experimentation and statistics.  Improve the team’s technical rigor

To thrive in this role, you have

• A strong orientation toward business impact — you ensure insights lead to measurable business results
• Demonstrated ability to influence senior stakeholders and guide strategic decisions in ambiguous, fast-moving environments
• PhD in statistics, economics, or similar social science with emphasis on quantitative methods
• 5+ years of experience in applied science roles, with deep expertise in predictive modeling and causal inference; Experience in FinTech and in Marketing strongly preferred
• Advanced skills in SQL and Python or R.  Experience in use of AI to accelerate and improve end-to-end process

#LI-Hybrid #LI-AM1

About the role

Chime is looking for a Senior Manager of Marketing Analytics to lead and scale our Marketing Analytics function during a critical phase of growth. In this role, you’ll set the vision for how marketing analytics drives business strategy.  You’ll lead a high-performing team using descriptive analytics, modeling, and experimentation to advise Marketing and Executive leadership on where and how to invest for maximum impact.

The base salary offered for this role and level of experience will begin at $188,000 and up to $260,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Drive business outcomes.  This role is critical for the Growth org, this team is meant to deliver outcomes not academic analyses that will sit on a shelf
• Set and implement the vision and strategy for the Marketing Analytics team
• Lead, mentor, and scale a team of marketing analysts, fostering high standards for technical rigor and business impact
• Prioritize the team’s work.  Improve the team’s processes using AI.
• Own the company’s marketing reporting and analysis for paid, owned, and integrated channels, and for performance across acquisition, activation, engagement, retention, and LTV
• Build scalable reporting infrastructure and self-serve tools that improve marketing velocity and accountability
• Use MMM, experimental, and quasi-experimental methodologies to estimate incremental impact
• Translate complex analyses into clear and persuasive executive-level recommendations.  Partner with Marketing, Finance, and Product leadership to inform budget allocation and investment strategy

To thrive in this role, you have

• 10+ years of experience in analytics or a related quantitative field, with 8+ years in Marketing Analytics within a B2C technology company
• A results-oriented mindset with a bias toward action, clarity, and high standards
• Exceptional stakeholder management skills and experience influencing senior leadership and cross-functional executives
• A demonstrated record of using data to drive measurable business impact in a consumer-facing marketing team.  Experience with brand and performance marketing and how marketing teams operate those functions
• Experience leading and developing high-performing analytics teams, with a track record of elevating talent and building strong culture
• Deep expertise in marketing measurement, including attribution modeling, media mix modeling, causal inference, customer lifetime value modeling
• Strong SQL proficiency and experience working with large-scale data warehouses
• Proficiency in BI and visualization tools (e.g., Looker, Tableau, PowerBI); experience improving data accessibility across organizations
• Strong statistical foundation (e.g., hypothesis testing, predictive modeling, experimental design), experience with Python

#LI-AM1 #LI-Hybrid

About the Role

Chime is the largest and fastest-growing U.S. player in the challenger-banking space. Through our banking partners, we offer access to bank accounts with fee-free overdraft, provide members the chance to receive early access to their paychecks, help them improve their credit, and more. We created Chime because we believe everyone deserves financial peace of mind.

We’re looking for a Lead Data Analyst to drive forecasting for key growth and revenue drivers and deliver insights that shape business decisions. You’ll own end-to-end forecasting workstreams such as member growth, purchase volume, deposit volume, and intramonth performance tracking - while partnering closely across Analytics, Data Science, Product, Marketing, and Investor Relations.

This role is ideal for someone who’s highly analytical and model-driven, but equally strong at turning numbers into a clear narrative that leaders can act on.

The base salary offered for this role and level of experience will begin at $152,000 and up to $210,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Own core forecasting workstreams for key business metrics (e.g., member growth, purchase volume, deposit volume), including in-year outlooks and contributions to longer-range forecasting.
• Build, maintain, and improve forecasting models that are accurate, transparent, and scalable; document assumptions and create repeatable processes.
• Run intramonth pacing, variance, and sensitivity analyses, identifying key drivers and surfacing risks/opportunities early.
• Develop scenarios and “what-if” analyses to support planning and real-time decision-making (e.g., upside/downside cases, impact of product or marketing changes).
• Partner cross-functionally to align inputs and assumptions, reconcile differences across reporting/forecasting views, and ensure a single source of truth where possible.
• Own unit economics and profitability analytics, including LTV/CAC, ROI analysis, and framework development for evaluating product and growth initiatives.
• Improve dashboards and forecasting tooling (spreadsheets, Looker/Pigment or similar) to increase visibility, consistency, and speed of insight.

To thrive in this role, you have

• 4–8 years of experience in forecasting, finance analytics, strategic finance, or a similar quantitative role (fintech/payments/consumer financial services is a plus).
• Demonstrated ability to own forecasting end-to-end and deliver decision-driving insights to senior stakeholders.
• Strong modeling instincts: comfortable living in the details (assumptions, drivers, sensitivities) while keeping the business story straight.
• Excellent communication: able to translate analytical output into crisp narratives, tradeoffs, and recommendations for technical and non-technical audiences.
• Expert SQL skills - able to translate business questions into robust queries, understand edge cases, and explore a warehouse efficiently to find the right data.
• Proficiency in Python or R - writes reproducible, well-structured code with a tendency toward automation and scalable analysis.
• High accountability, strong judgment, and comfort operating in ambiguity and fast-moving environments.

#LI-Hybrid #LI-BE1

About the Role

Chime is the largest and fastest-growing U.S. player in the challenger-banking space. Through our banking partners, we offer access to bank accounts with fee-free overdraft, provide members the chance to receive early access to their paychecks, help them improve their credit, and more. We created Chime because we believe everyone deserves financial peace of mind.

We’re looking for a Staff Data Analyst to lead critical forecasting and performance insights for Chime’s core growth and revenue drivers. In this role, you’ll set the strategy and operating model for forecasting across key metrics (e.g., member growth, purchase volume, deposit volume), drive scenario planning and investment tradeoffs with senior leadership, and elevate how Chime measures and communicates performance and unit economics.

You’ll operate at the intersection of Finance, Strategy, Analytics, and Product—partnering closely with Finance leadership and collaborating across Marketing, Product, Operations & Member Experience, Risk, and Investor Relations. Your work will inform high-impact decisions and help ensure Chime has scalable forecasting capabilities as the business matures.

The base salary offered for this role and level of experience will begin at $170,000 and up to $235,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Own end-to-end forecasting strategy and operating rhythm for core business and financial drivers, including in-year outlooks, long-range planning inputs, and assumption governance.
• Define and evolve forecasting frameworks that improve accuracy, transparency, and consistency - standardizing templates, processes, and documentation across teams.
• Drive scenario planning and investment tradeoff discussions with senior leaders (e.g., “Where should we invest for the highest impact, and what are the expected outcomes and risks?”), including sensitivities and decision-ready recommendations.
• Create performance narratives for key KPIs and unit economics, ensuring leaders have clear, consistent readouts on what’s changing, why it’s changing, and what actions are required.
• Be the arbiter of truth for forecasting and performance measurement, aligning stakeholders on metric definitions, reconciling differences across sources, and raising the bar on analytical rigor and clarity.
  Partner deeply across the company—work hand-in-hand with Finance and influence leaders across Marketing, Product, Operations & Member Experience, Risk, and Investor Relations to align assumptions, drive accountability, and accelerate decisions.
• Scale insights and tooling—own development of dashboards, automation, and self-serve analytics (e.g., Looker/Tableau/Pigment, SQL/Python/R pipelines) that improve speed-to-insight and reduce manual effort.
• Develop talent and raise the bar by managing, mentoring and coaching managers / analysts and setting best practices for modeling, communication, and stakeholder management.

To thrive in this role, you have

• 8–10 years in strategic finance, forecasting/FP&A, finance analytics, or a similarly analytical finance role (fintech/payments/consumer financial services strongly preferred).
• Track record of leading complex forecasting and planning workstreams and influencing senior leaders through structured tradeoffs and decision-ready narratives.
• Deep fluency in financial metrics, unit economics, and performance measurement, including how investors evaluate business performance.
• Strong modeling and analytical skills; able to connect operational drivers to financial outcomes and communicate uncertainty appropriately.
• Advanced SQL and spreadsheet modeling; Python or R proficiency for reproducible analysis and automation strongly preferred.
• Exceptional communication and stakeholder leadership—able to align cross-functional teams, resolve ambiguity, and drive decisions without relying on authority.
• Strong ownership and pace; comfortable operating in a high-growth environment and continuously improving systems and processes.

#LI-Hybrid #LI-BE1

About the role

We are looking for a Sr. Full Stack Application Security Engineer with deep expertise in mobile application security to join our Product Security team. This role is hands-on and impact driven. You will work directly with mobile, backend, and platform engineering teams to identify, prevent, and remediate security issues across our iOS, Android, API, and backend systems.

You will operate close to the code and close to the product. That means reviewing architectures across the stack, influencing secure design decisions early, and helping teams ship features safely without slowing delivery. This role is for someone who understands how modern distributed systems and mobile apps are built, deployed, and attacked in real-world environments.

While mobile application security is a core focus, you will be part of a team that owns security posture across the full application stack including APIs, backend services, identity and authentication flows, and CI/CD pipelines.

The base salary offered for this role and level of experience will begin at $213,000 and up to $295,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. 

In this role, you can expect to

• Build and improve security capabilities, automation, and guardrails for mobile applications and backend/API services
• Perform application or API/backend penetration testing 
• Identify, triage, and help remediate vulnerabilities across Chime products
• Partner closely with engineering and product teams to embed security into the development lifecycle across mobile apps, APIs, and backend services
• Perform architecture and code reviews across the stack (iOS/Android, APIs, backend) with a focus on secure data storage, authentication, authorization, secure communication, and session/token handling
• Leverage AI to accelerate security workflows (e.g., code review support, triage, threat modeling), and partner with teams building AI-enabled features to define and implement production-grade AI security controls 

To thrive in this role, you have

• 5+ years of experience in application security, with strong hands-on experience across both mobile and backend systems 
• Hands on experience securing iOS and Android applications in production environments
• Strong understanding of mobile threat models and common attack techniques
• Experience with mobile security testing techniques, including static and dynamic analysis
• Familiarity with iOS and Android platform security features and limitations
• Practical coding experience, preferably in Ruby, Go, Python  languages
• Ability to clearly communicate security risks, tradeoffs, and remediation guidance to engineering partners

#LI-Hybrid #LI-JL1

About the role

We are looking for a Software Engineer to help build our new financial platform that is highly reliable, scalable, and will serve as the backbone for Chime. This platform will power the most important financial activities that millions of Chime members undertake every day. You will ensure that we build the highest levels of trust with our members by making our platform lightning-fast, available, secure, resilient and auditable. This is an exciting opportunity to be an invaluable member of a strategically important  initiative for Chime. 

The base salary offered for this role and level of experience will begin at $133,000 and up to $184,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• Design, develop, test, and deploy components of a new financial platform
• Deliver secure, reliable, and high-performance infrastructure and APIs
• Work with our infrastructure engineering team to minimize the operational overhead of production systems you deploy
• Develop thoroughly tested code and conduct quality assurance on all new code alongside our QA team
• Learn and execute on agile methodologies, follow test driven development approach
• Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

To thrive in this role, you have

• Must have experience in Go
• Experience in additional backend languages such as Ruby, Java and Python is preferred
• Have 4+ years of engineering experience
• Have contributed to the development of backend systems
• Experience in payment processing systems
• Take a rigorous approach to testing backed development
• Experience with AWS or other cloud platforms, Cloud Native technology, No-SQL Databases as a bonus!
• Have an understanding of architectural patterns of large, mission-critical production services as a bonus!
• Have worked in continuous integration/deployment environments as a bonus!
• Please note: If you are located in Seattle, you will be required to onboard in person at our SF HQ + occasional business travel.

#LI-Onsite #LI-GC1

About the role

We are looking for an early-career Security Engineer to join our Product Security team,  someone who has a builder’s mindset, is eager to learn, and is excited to contribute to both planned initiatives and dynamic, real-time security needs with enough guidance and mentorship.

In this role, you’ll be part of a hands-on security team working across Infrastructure, Application, and Enterprise Security where responsibilities sometimes overlap. You’ll contribute to the design and implementation of security controls and solutions, review technical designs, and respond to findings from security researchers and penetration testers. You’ll also write code and build automation, using AI tools to help accelerate your work and strengthen security practices. Along the way, you’ll gain exposure to a wide range of security challenges that will accelerate your growth as a security engineer.

The base salary offered for this role and level of experience will begin at $130,000and up to $180,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. 

In this role, you can expect to 

• Develop assistive automation and workflows to streamline the detection and reporting of security findings
• Contribute to tracking and remediation of discovered security vulnerabilities
• Participate in projects that reduce security risks and attack surface within our infrastructure and corporate applications
• Get the opportunity to use top notch AI systems in your daily work and play a role in strengthening their security

To thrive in this role, you have

• 1–3 years of experience in a security role, or in a development role with a focus on or enthusiasm for security
• Flexibility and a willingness to take on a variety of tasks in different security domains
• Practical coding experience preferably in Go, Python, or Ruby
• Hands-on experience with AWS and ideally GCP
• Exposure to cloud and application security concepts
• Experience with Terraform (a plus)

#LI-Hybrid #LI-JL1

About the Role

The Turbo team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale.

Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design.

You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal.

Requirements

We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay.

You might be a good fit if you:

• Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:
• Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving.
• RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models.
• Model architecture design for Transformers or other large neural nets.
• Distributed systems / high‑performance computing for ML.
• Are comfortable working from algorithms to engines:
• Strong coding ability in Python
• Experience profiling and optimizing performance across GPU, networking, and memory layers.
• Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack.
• Have a solid research foundation in your area(s) of depth:
• Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems).
• Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API).
• Operate well as a full‑stack problem solver:
• You naturally ask: “Where in the stack is this really bottlenecked?”
• You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins.

Minimum qualifications

• 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source).
• Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience.
• Demonstrated experience owning complex technical projects end‑to‑end.

If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement.

Responsibilities

• Advance inference efficiency end‑to‑end
• Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference.
• Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc.
• Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost.
• Unify inference with RL / post‑training
• Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems.
• Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper.
• Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack.
• Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers.
• Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design.
• Own critical systems at production scale
• Profile, debug, and optimize inference and post‑training services under real production workloads.
• Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed.
• Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously.
• Provide technical leadership (Staff level)
• Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training.
• Mentor other engineers and researchers on full‑stack ML systems work and performance engineering.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

You’ll join the Data Platform team, responsible for building the backend services and “data products” that power how data moves through the company. We create the core platform primitives - high-quality event streams, reliable access layers, and developer-friendly APIs/tools - so teams across the org can self-serve what they need and ship faster. You’ll design, build, and run backend services that create value from our company data, and make our data platform more self-serve (DIY)- so product and engineering teams can easily create and operate event-driven architectures, publish/consume streams, define access models, and ship data products end-to-end. You’ll also build LLM-adjacent services such as prompt categorization/taxonomy, enrichment, and metadata systems that turn raw telemetry into trusted, usable products.

Responsibilities

• Identify, design, and develop foundational data infrastructure components capable of handling millions or billions of events daily
• Analyze and improve the robustness and scalability of existing data processing infrastructure
• Partner with product teams to understand functional requirements and deliver solutions that meet business needs
• Write clear, well-tested, and maintainable infra-as-code and software for both new and existing systems
• Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance
• Participate in an on-call rotation to address critical incidents when necessary

Requirements

• 6+ years building production backend systems (distributed systems, APIs, data services).
• Strong skills in at least one backend language (e.g., Go, Python, Java, Rust) and API design (REST/gRPC).
• Demonstrated ability to own services end-to-end: design docs, implementation, testing, deployment, on-call, and iteration based on real usage.
• Strong engineering fundamentals: clean, maintainable code, thoughtful abstractions, strong design patterns, and systems that are easy to evolve.
• Solid data modeling and query skills (SQL) and experience with at least one of:

• • Streaming/eventing (Kafka/PubSub/Kinesis, etc.)
  • Workflow/compute (Airflow/Spark/Flink/Trino, etc.)
  • OLTP/OLAP stores and data lakes (Postgres + warehouse/lake tech)

• AI augmentation mindset:
  • You understand the state-of-the-art ways engineers use AI/LLMs to build software faster and better (e.g., coding copilots, agentic workflows, eval-driven iteration, retrieval/knowledge grounding), and you apply this thoughtfully to your own development and to the systems you build.
  • You have engineering judgment and you understand when AI Augmentation can fail or create team dynamics issues, putting mitigation mechanisms and guardrails in place.

Nice to have

• Experience building self-serve platforms (control planes, templates/golden paths, paved roads, multi-tenant services).
• LLM/AI product exposure: prompt/response telemetry, eval datasets, embeddings/RAG metadata, model/tool traces, privacy-safe logging.
• Security and governance experience: least-privilege access, auditability, data retention, PII handling.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $240,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

This is the first dedicated data hire on Together AI's Finance team. You will own the data layer that Finance runs on — building from scratch the models, pipelines, and reporting infrastructure that allow Strategic Finance, FP&A, and Accounting teams to get reliable answers quickly. The person in this role will have direct exposure to every part of the Finance organization and a real opportunity to shape how data-driven decision-making develops here as the company scales.

A significant portion of your work will touch the data behind the economics of Together's infrastructure, which sits behind nearly every financial question we ask. You will work closely with Together's Data and Commerce engineering team, which owns the underlying billing pipelines and data warehouse. Your job is to define and build the modeling and reporting layer that turns raw operational data into finance-grade datasets — aligning on data contracts, representing Finance's requirements in data design decisions, and ensuring the metrics Finance depends on are correct, documented, and trusted.

Responsibilities

• Own and evolve the dbt transformation layer for Finance: design, build, test, document, and maintain models covering billing, financial performance, compute unit economics, and operational metrics
• Author and maintain Airflow DAGs (Astronomer-managed) that orchestrate Finance dbt runs, data quality checks, and downstream dependencies reliably
• Deliver dashboards and reporting in Hex for the executive team covering financial performance, utilization, and key operating metrics
• Partner with Strategic Finance, FP&A, and Accounting teams on the data infrastructure behind forecasting, cost modeling, and other financial analyses
• Set data quality standards across Finance data products and own incident response when Finance-critical pipelines break
• Build the data foundation – clean, well-structured, documented, and reliably maintained – that enables Finance team to self-serve data analysis and supports AI automation and agentic workflows 

Requirements

• 5+ years in an analytics engineering or data engineering role, with meaningful time supporting Finance or business operations teams
• Expert SQL and production-grade dbt experience — models, tests, docs, snapshots, incremental strategies; you've designed a dbt project from scratch and maintained it
• Hands-on experience with Snowflake and Airflow in production; familiarity with Astronomer and the Cosmos dbt integration is a strong plus
• Solid dimensional modeling fundamentals: star schemas, slowly changing dimensions, fact table grain — you can apply Kimball principles without needing to look them up
• Strong dashboarding skills with a track record of executive-facing reporting that people actually use; experience with Hex or a comparable notebook/BI tool
• Clear communicator who can align stakeholders on metric definitions and translate between engineering tradeoffs and Finance requirements in both directions
• High comfort with ambiguity; this role will require scoping problems from scratch, not executing against pre-defined specs

Nice to Have

• Experience with financial data or billing data such as ARR, usage-based billing, invoice reconciliation, revenue recognition patterns, etc.. We operate a usage-based billing system and this context transfers directly.
• Experience with PII handling, data masking, access-tier modeling, or compliance work (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
• Experience with Hex, Metabase, or similar notebook/BI tooling that sits on top of your dbt models.
• Prior experience in a high-growth AI/ML infrastructure or platform company

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200k -$240k + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy.  

About the role

As a Forward Deployed Engineer (FDE) focused on large scale GPU clusters, you will be a hands-on technical partner to our strategic customers – the world’s leading AI model builders. You will partner with our SAs as a deep-domain specialist in large-scale infrastructure, storage, high-performance networking, and cluster orchestration. As key contributors to the CX, Engineering, and Sales organizations, FDEs add tremendous value by ensuring we can meet the requirements of our most complex POCs, facilitate successful platform adoption for our strategic customers, and guide tailored optimization efforts - directly impacting company growth and the hardening of our core platform.

Responsibilities

• Cluster Hardening & Validation: Design and execute rigorous pre-handover test suites (NCCL, DCGM, GPU Burn) to ensure clusters are stable under the extreme stress of multi-node training.
• Technical Partnership: Act as the primary technical point of contact for model labs, helping them tune their orchestration layer (Kubernetes or SLURM) for maximum throughput.
• Infrastructure Optimization: Profile and debug low-level bottlenecks in InfiniBand (IB) fabrics, NVLink topologies, and high-performance storage systems.
• Opinionated Onboarding: Build reference designs and "out-of-the-box" configurations for training frameworks to reduce customer time-to-train.
• Benchmarking & Migration: Lead complex benchmarking exercises to demonstrate the performance impact of migrating to new hardware families or Together AI’s optimized infrastructure.
• Product Feedback Loop: Directly influence our hardware and software roadmap by surfacing edge cases and performance gaps found during customer deployments.

Requirements

• Experience: 5+ years in a technical role, with a strong focus on Large-Scale GPU Infrastructure.
• Orchestration Mastery: Deep, hands-on experience with Kubernetes (specifically GPU-operator and device plugins) and/or SLURM for workload scheduling.
• Networking & Interconnects: Expert knowledge of InfiniBand, RoCE, and NVLink; ability to diagnose network failures that degrade collective communication (NCCL).
• Storage Knowledge: Familiarity with parallel file systems (VAST or Weka preferred) and object storage, specifically in the context of large-scale checkpointing.
• Benchmarking Skills: Ability to run and interpret training benchmarks and communication tests to validate cluster health and performance.
• Coding & Automation: Proficiency in Python and shell scripting; experience with Ansible or similar tools for automated cluster configuration.
• Willingness to dive into the customer's stack to solve hard problems and comfortable with the high-stakes, fast-paced environment of frontier model labs.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers on our journey in building the next generation of AI infrastructure. 

Compensation

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $270,000 - $300,000 OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. 

Equal Opportunity

Together AI is an 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.

Please see our Privacy Policy at https://www.together.ai/privacy

About the role

As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to our most strategic customers — production AI teams looking to leverage high quality models and do inference at scale. For us, FDE is not a replacement for a Solutions Architect; you will partner with our SAs as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment. As key contributors to both the CX, Engineering, and Sales organizations, FDEs add tremendous value by ensuring we can meet the requirements of our most complex POCs, facilitate successful platform adoption, and guide tailored optimization efforts — directly impacting customer success, company growth, and the hardening of our core platform.

Responsibilities

• Inference Engine Optimization: Select, configure, and optimize inference engine based on hardware, model architecture, and workload profile
• Configuration & Performance Tuning: Develop configuration updates to win critical POCs, benchmarks, and optimize customer deployments; tune KV cache, apply speculative decoding, determine optimal tensor parallelism, and determine quantization strategy to hit throughput and latency targets.
• Post-Training & Fine-Tuning: Drive hands-on RL training runs and optimize system design; guide customers through LoRA, SFT, DPO, RLHF, and GRPO pipelines from experimentation through production.
• Strategic Customer Alignment: Act as the primary technical point of contact for aligned strategic accounts — monitoring and optimizing endpoint configurations, helping customers get the most out of the platform, and collaborating to ensure we hit critical milestones.
• Opinionated Onboarding: Establish direct alignment with strategic customers at onboarding; ensure the right inference and post-training configurations are in place from day one to improve time-to-value.
• Product Feedback Loop: Directly influence our software and model roadmap by surfacing insights from the field. Contribute back to the product where needed to support customer requirements or drive a better experience. Drive early feature and research adoption with strategic logos.

Qualifications

• Experience: 5+ years in a technical role, with a strong focus on inference systems, open-source LLM deployment, or post-training workflows.
• Inference Engine Depth: Expert-level, hands-on experience with inference engines (e.g., vLLM, TensorRT-LLM, SGLang); ability to diagnose and resolve performance issues at the engine level.
• Inference Optimization: Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization techniques
• Post-Training Knowledge: Hands-on experience with fine-tuning and post-training pipelines, including LoRA, SFT, DPO, RLHF, and GRPO; ability to advise on system design
• Model Landscape Awareness: Broad knowledge of state-of-the-art open-source models and strong judgment on model selection for specific customer use cases, hardware profiles, and performance targets.
• Coding Proficiency: Strong Python skills; comfortable working in production environments

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers on our journey in building the next generation of AI infrastructure. 

Compensation

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $270,000 - $300,000 OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. 

Equal Opportunity

Together AI is an 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.

Please see our Privacy Policy at https://www.together.ai/privacy

About the Role

At Together.ai, we are building state-of-the-art infrastructure to enable efficient and scalable inference for large language models (LLMs). Our mission is to optimize inference frameworks, algorithms, and infrastructure, pushing the boundaries of performance, scalability, and cost-efficiency.

We are seeking an Inference Frameworks and Optimization Engineer to design, develop, and optimize distributed inference engines that support multimodal and language models at scale. This role will focus on low-latency, high-throughput inference, GPU/accelerator optimizations, and software-hardware co-design, ensuring efficient large-scale deployment of LLMs and vision models.

This role offers a unique opportunity to shape the future of LLM inference infrastructure, ensuring scalable, high-performance AI deployment across a diverse range of applications. If you're passionate about pushing the boundaries of AI inference, we’d love to hear from you!

Responsibilities

Inference Framework Development and Optimization

• Design and develop fault-tolerant, high-concurrency distributed inference engine for text, image, and multimodal generation models.
• Implement and optimize distributed inference strategies, including Mixture of Experts (MoE) parallelism, tensor parallelism, pipeline parallelism for high-performance serving.
• Apply CUDA graph optimizations, TensorRT/TRT-LLM graph optimizations, and PyTorch-based compilation (torch.compile), and speculative decoding to enhance efficiency and scalability.

Software-Hardware Co-Design and AI Infrastructure

• Collaborate with hardware teams on performance bottleneck analysis, co-optimize inference performance for GPUs, TPUs, or custom accelerators.
• Work closely with AI researchers and infrastructure engineers to develop efficient model execution plans and optimize E2E model serving pipelines.

Requirements

Must-Have:

• Experience:
  • 3+ years of experience in deep learning inference frameworks, distributed systems, or high-performance computing.
• Technical Skills:
  • Familiar with at least one LLM inference frameworks (e.g., TensorRT-LLM, vLLM, SGLang, TGI(Text Generation Inference)).
  • Background knowledge and experience in at least one of the following: GPU programming (CUDA/Triton/TensorRT), compiler, model quantization, and GPU cluster scheduling.
  • Deep understanding of KV cache systems like Mooncake, PagedAttention, or custom in-house variants.
• Programming:
  • Proficient in Python and C++/CUDA for high-performance deep learning inference.
• Optimization Techniques:
  • Deep understanding of Transformer architectures and LLM/VLM/Diffusion model optimization.
  • Knowledge of inference optimization, such as workload scheduling, CUDA graph, compiled, efficient kernels
• Soft Skills:
  • Strong analytical problem-solving skills with a performance-driven mindset.
  • Excellent collaboration and communication skills across teams.

Nice-to-Have:

• Experience in developing software systems for large-scale data center networks with RDMA/RoCE
• Familiar with distributed filesystem(e.g., 3FS, HDFS, Ceph)
• Familiar with open source distributed scheduling/orchestration frameworks, such as Kubernetes (K8S)
• Contributions to open-source deep learning inference projects.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is looking for an ML Engineer who will develop systems and APIs that enable our customers to perform inference and fine tune LLMs. Relevant experience includes implementing runtime systems that perform inference at scale using AI/ML models from simple models up to the largest LLMs.

Requirements

• 5+ years experience writing high-performance, well-tested, production quality code
• Bachelor’s degree in computer science or equivalent industry experience
• Familiar with LLM inference ecosystem, including frameworks and engines (e.g. vLLM, SGLang, TRT, ...)
• Demonstrated experience in building large scale, fault tolerant, distributed systems like storage, search, and computation
• Expert level programmer in one or more of Python, Go, Rust, or C/C++
• Experience implementing runtime inference services at scale or similar

Responsibilities

• Design and build the production systems that power the Together Cloud inference and fine-tuning APIs, enabling reliability and performance at scale
• Partner with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world
• Analyze and improve efficiency, scalability, and stability of various system resources
• Conduct design and code reviews
• Create services, tools & developer documentation
• Create testing frameworks for robustness and fault-tolerance
• Participate in an on-call rotation to respond to critical incidents as needed

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $220,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is seeking a Machine Learning Engineer to join our Inference Engine team, focusing on optimizing and enhancing the performance of our AI inference systems. This role involves working with state-of-the-art large language models models and ensuring they run efficiently and effectively at scale. If you are passionate about AI inference, PyTorch, and developing high-performance systems, we want to hear from you. This position offers the chance to collaborate closely with AI researchers and engineers to create cutting-edge AI solutions. Join us in shaping the future at Together AI!

Responsibilities

• Design and build the production systems that power the Together AI inference engine, enabling reliability and performance at scale.
• Develop and optimize runtime inference services for large-scale AI applications.
• Collaborate with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world.
• Conduct design and code reviews to ensure high standards of quality.
• Create services, tools, and developer documentation to support the inference engine.
• Implement robust and fault-tolerant systems for data ingestion and processing.

Requirements

• 3+ years of experience writing high-performance, well-tested, production-quality code.
• Proficiency with Python and PyTorch.
• Demonstrated experience in building high performance libraries and tooling.
• Excellent understanding of low-level operating systems concepts including multi-threading, memory management, networking, storage, performance, and scale.
• Preferred: Knowledge of existing AI inference systems such as TGI, vLLM, TensorRT-LLM, Optimum
• Preferred: Knowledge of AI inference techniques such as speculative decoding.
• Preferred: Knowledge of CUDA/Triton programming.
• Nice to have: Knowledge of Rust, Cython and compilers.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society. Together, we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI. Our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey to build the next-generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance, and other competitive benefits. The US base salary range for this full-time position is $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level, and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunities to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

About the Role

Our team focuses on enabling custom models and dedicated inference on Together. We are responsible for building a container platform, optimizing autoscaling, minimizing cold starts, achieving the best end-to-end model performance, and providing a best-in-class developer experience with great tooling. We often focus on video or audio generation across the stack: CUDA kernels, pytorch optimization, inference engines, container orchestration, queueing theory, etc. An ideal candidate will be great at profiling/optimization but know the word kubernetes, or be intimately familiar with multi-cluster scheduling and have some sense of ML bottlenecks.

Responsibilities

• New hires may work on multi-cluster orchestration, portfolio optimization, predictive autoscaling, control panes, model bring-up, model optimization, APIs for managing deployments, inference worker SDKs, and CLI tools.
• Analyze and improve the robustness and scalability of existing distributed systems, APIs, databases, and infrastructure
• Partner with product teams to understand functional requirements and deliver solutions that meet business needs
• Write clear, well-tested, and maintainable software and IaC for both new and existing systems
• Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance

Requirements

• 5+ years of demonstrated experience in building large scale, fault tolerant, distributed systems.
• Experience running serverless inference platforms, doing model bring-up on short notice, being on call, or running a cloud provider is a very big plus
• Good taste and ability to thoughtfully discuss how what you’ve built has failed over time
• Experience designing, analyzing and improving efficiency, scalability, and stability of various system resources
• Excellent understanding of low level operating systems concepts including concurrency, networking and storage, performance and scale
• Expert-level programmer in one or more of Python, Golang, Rust, C++, or Haskell
• Proficiency in writing and maintaining Infrastructure as Code (IaC) using tools like Terraform
• Experience with Kubernetes internals or other container orchestration systems
• Sound judgement for when to use and when to not use LLMs for code
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
• Writing-heavy roles or companies are a plus

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $250,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

This is a research engineering role with direct production impact. You won’t be publishing ideas in isolation—you will translate new RL algorithms, scheduling methods, and inference optimizations into production-grade systems that power Together’s API. Success in this role means shipping measurable improvements in latency, throughput, cost, and model quality at scale. We are looking for researchers who enjoy owning systems end-to-end and turning frontier ideas into robust infrastructure.

The Core ML (Turbo) at Together AI team sits at the intersection of efficient inference (algorithms, architectures, engines) and post‑training / RL systems. We build and operate the systems behind Together’s API, including high‑performance inference and RL/post‑training engines that can run at production scale.

Our mandate is to push the frontier of efficient inference and RL‑driven training: making models dramatically faster and cheaper to run, while improving their capabilities through RL‑based post‑training (e.g., GRPO‑style objectives). This work lives at the interface of algorithms and systems: asynchronous RL, rollout collection, scheduling, and batching all interact with engine design, creating many knobs to tune across the RL algorithm, training loop, and inference stack. Much of the job is modifying production inference systems—for example, SGLang‑ or vLLM‑style serving stacks and speculative decoding systems such as ATLAS—grounded in a strong understanding of post‑training and inference theory, rather than purely theoretical algorithm design.

You’ll work across the stack—from RL algorithms and training engines to kernels and serving systems—to build and improve frontier models via RL pipelines. People on this team are often spiky: some are more RL‑first, some are more systems‑first. Depth in one of these areas plus appetite to collaborate across (and grow toward more full‑stack ownership over time) is ideal.

Responsibilities

• Advance inference efficiency end‑to‑end
• Design and prototype algorithms, architectures, and scheduling strategies for low‑latency, high‑throughput inference.
• Implement and maintain changes in high‑performance inference engines (e.g., SGLang‑ or vLLM‑style systems and Together’s inference stack), including kernel backends, speculative decoding (e.g., ATLAS), quantization, etc.
• Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost.
• Unify inference with RL / post‑training
• Design and operate RL and post‑training pipelines (e.g., RLHF, RLAIF, GRPO, DPO‑style methods, reward modeling) where 90+% of the cost is inference, jointly optimizing algorithms and systems.
• Make RL and post‑training workloads more efficient with inference‑aware training loops—for example, async RL rollouts, speculative decoding, and other techniques that make large‑scale rollout collection and evaluation cheaper.
• Use these pipelines to train, evaluate, and iterate on frontier models on top of our inference stack.
• Co‑design algorithms and infrastructure so that objectives, rollout collection, and evaluation are tightly coupled to efficient inference, and quickly identify bottlenecks across the training engine, inference engine, data pipeline, and user‑facing layers.
• Run ablations and scale‑up experiments to understand trade‑offs between model quality, latency, throughput, and cost, and feed these insights back into model, RL, and system design.
• Own critical systems at production scale
• Profile, debug, and optimize inference and post-training services under real production workloads, taking research ideas all the way to stable, measurable improvements in deployed systems.
• Drive roadmap items that require real engine modification—changing kernels, memory layouts, scheduling logic, and APIs as needed.
• Establish metrics, benchmarks, and experimentation frameworks to validate improvements rigorously.
• Provide technical leadership (Staff level)
• Set technical direction for cross‑team efforts at the intersection of inference, RL, and post‑training.
• Mentor other engineers and researchers on full‑stack ML systems work and performance engineering.

Requirements

We don’t expect anyone to check every box below. People on this team typically have deep expertise in one or more areas and enough breadth (or interest) to work effectively across the stack. The closer you are to full‑stack (inference + post‑training/RL + systems), the stronger the fit—but being spiky in one area and eager to grow is absolutely okay.

You might be a good fit if you:

• Have a bias toward implementation and shipping—you are excited to modify real engines and services, not just prototype in research code.
• Have strong expertise in at least one of the following, and are excited to collaborate across (and grow into) the others:
• Systems‑first profile: Large‑scale inference systems (e.g., SGLang, vLLM, FasterTransformer, TensorRT, custom engines, or similar), GPU performance, distributed serving.
• RL‑first profile: RL / post‑training for LLMs or large models (e.g., GRPO, RLHF/RLAIF, DPO‑like methods, reward modeling), and using these to train or fine‑tune real models.
• Model architecture design for Transformers or other large neural nets.
• Distributed systems / high‑performance computing for ML.
• Are comfortable working from algorithms to engines:
• Strong coding ability in Python
• Experience profiling and optimizing performance across GPU, networking, and memory layers.
• Able to take a new sampling method, scheduler, or RL update and turn it into a production‑grade implementation in the engine and/or training stack.
• Have a solid research foundation in your area(s) of depth:
• Track record of impactful work in ML systems, RL, or large‑scale model training (papers, open‑source projects, or production systems).
• Can read new RL / post‑training papers, understand their implications on the stack, and design minimal, correct changes in the right layer (training engine vs. inference engine vs. data / API).
• Operate well as a full‑stack problem solver:
• You naturally ask: “Where in the stack is this really bottlenecked?”
• You enjoy collaborating with infra, research, and product teams, and you care about both scientific quality and user‑visible wins.

Minimum qualifications

• 3+ years of experience working on ML systems, large‑scale model training, inference, or adjacent areas (or equivalent experience via research / open source).
• Advanced degree in Computer Science, EE, or a related field, or equivalent practical experience.
• Demonstrated experience owning complex technical projects end‑to‑end.

If you’re excited about the role and strong in some of these areas, we encourage you to apply even if you don’t meet every single requirement.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy

About the Role

Together AI is building the Inference Platform that powers the world's most advanced generative AI models. Your role will be a critical bridge between cutting-edge research and real-world applications, focusing on making translating our internal model training research to production-ready deployment for our customers. This involves a deep commitment to data-centric development, meticulous hyperparameter tuning, and rigorous checkpoint evaluation before models ever hit production.

This role will involve understanding customer specific needs and fine-tuning models on our internal data recipe and their proprietary data. The goal is to transform general-purpose models into highly performant, specialized tools that solve real business problems. You will not be training foundation models from scratch but rather focusing on creating highly efficient, specialized models by working with dedicated GPU clusters.

Responsibilities

• Design and iterate on novel speculator algorithms, combining architectural innovations with carefully curated data to push the frontier of accuracy–efficiency tradeoffs.
• Be the critical link between raw data and a production-ready model, seeing your work directly impact our customers' success.
• Work in a fast-paced, high-impact role at the cutting edge of generative AI.
• Collaborate with a team of experts dedicated to solving real-world, high-performance challenges.
• You'll collaborate directly with customers to understand their needs, and work closely with our core inference and Applied ML research teams to integrate your work into the production platform.
• A culture of deep technical ownership where you are empowered to take on and solve challenging problems

Requirements

• A genuine love for data curation and processing, with a meticulous attention to detail. You believe that great models start with great data.
• Demonstrated ability to perform effective hyperparameter searches and understand the trade-offs involved in tuning models for specific tasks.
• Experience working with and building on top of existing training codebases. You are comfortable navigating complex code and contributing to its improvement.
• Strong attention-to-detail in evaluating model checkpoints to ensure they meet strict quality, performance, and reliability standards.
• Experience with Python and PyTorch.
• Familiarity with SLURM and/or Kubernetes clusters and experience submitting and managing jobs in a high-performance computing environment.
• Familiarity with modern LLMs and generative models.
• Basic understanding of distributed training frameworks (e.g., FSDP, DeepSpeed).
• Bachelor’s, Master’s degree, or Ph.D. in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, ATLAS, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $190,000 - $270,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy

About The Role

We are seeking a highly analytical and process-driven Sales and Marketing Operations Manager to join our Revenue Strategy and Operations team. In this pivotal role, you will bridge the gap between marketing strategy, sales execution, and data-driven decision-making. The ideal candidate will optimize the entire lead-to-revenue funnel, streamlining operations and leveraging data to enhance overall business performance.

Responsibilities

• Full-Funnel Reporting: Develop, maintain, and automate integrated dashboards that provide actionable insights into the entire customer journey, from initial marketing touchpoints to closed-won sales.
• Performance Analysis: Analyze data across the full tech stack (CRM, Marketing Automation, Web Analytics) to identify trends, conversion bottlenecks, and high-growth opportunities.
• Attribution & ROI: Collaborate with finance and leadership to refine attribution models, ensuring clear visibility into marketing's contribution to the sales pipeline and overall ROI.
• Forecasting: Define and track key performance indicators (MQLs, SQLs, Win Rates, CAC, LTV) to provide accurate revenue forecasts and identify potential risks.
• Tech Stack Management: Oversee and optimize our integrated technology stack, including Salesforce, marketing automation (e.g., HubSpot), and sales engagement tools (e.g., MixMax).
• Process Optimization: Design and implement scalable workflows for lead routing, lead scoring, and automated nurturing programs to ensure seamless handoffs between teams.
• Data Integrity: Maintain a "source of truth" by ensuring data cleanliness, implementing data enrichment strategies, and managing audience segmentation for targeted campaigns.
• Strategic Alignment: Act as the primary liaison between Sales and Marketing to ensure alignment on definitions, goals, and execution strategies.
  
  

Requirements

• Education: Bachelor’s degree in Marketing, Business, Statistics, Computer Science, or a related quantitative field.
• Experience: 5–7 years of experience in Sales/Marketing Operations or Business Intelligence, preferably within a B2B SaaS environment.
• Technical Proficiency: Expert-level experience with Marketing Automation Platforms (HubSpot, Marketo, Pardot) and CRMs (Salesforce).
• Data Visualization: Proven ability to create impactful dashboards using tools like Tableau, Power BI, Looker, or Google Data Studio.
• Analytical Skills: Exceptional ability to translate complex data sets into clear, actionable insights for non-technical stakeholders.
• Advanced Tools: Advanced Excel skills are required; proficiency in SQL, R, or Python for advanced data extraction is a strong plus.
• Strategic Mindset: Deep understanding of funnel mechanics, lead management, and the tactical requirements of sales and marketing alignment.
  
  

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $150,000 - $170,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy

About the Role

Together AI is building the Inference Platform that brings the most advanced generative AI models to the world. Our platform powers multi-tenant serverless workloads and dedicated endpoints, enabling developers, enterprises, and researchers to harness the latest LLMs, multimodal models, image, audio, video, and speech models at scale.

If you get a thrill from optimizing latency down to the last millisecond, this is your playground. You’ll work hands-on with tens of thousands of GPUs (H100s, H200s, GB200s, and beyond), figuring out how to fully utilize every FLOP and every gigabyte of memory.

You’ll collaborate directly with research teams to bring frontier models into production, making breakthroughs usable in the real world. Our team also works closely with the open source community, contributing to and leveraging projects like SGLang, vLLM, and NVIDIA Dynamo to push the boundaries of inference performance and efficiency.

• Shape the core inference backbone that powers Together AI’s frontier models.
• Solve performance-critical challenges in global request routing, load balancing, and large-scale resource allocation.
• Work with state-of-the-art accelerators (H100s, H200s, GB200s) at global scale.
• Partner with world-class researchers to bring new model architectures into production.
• Collaborate with and contribute to the open source community, shaping the tools that advance the industry.
• A culture of deep technical ownership and high impact — where your work makes models faster, cheaper, and more accessible.
• Competitive compensation, equity, and benefits.

Responsibilities 

• Build and optimize global and local request routing, ensuring low-latency load balancing across data centers and model engine pods.
• Develop auto-scaling systems to dynamically allocate resources and meet strict SLOs across dozens of data centers.
• Design systems for multi-tenant traffic shaping, tuning both resource allocation and request handling — including smart rate limiting and regulation — to ensure fairness and consistent experience across all users.
• Engineer trade-offs between latency and throughput to serve diverse workloads efficiently.
• Optimize prefix caching to reduce model compute and speed up responses.
• Collaborate with ML researchers to bring new model architectures into production at scale.
• Continuously profile and analyze system-level performance to identify bottlenecks and implement optimizations.

Requirements

• 5+ years of demonstrated experience building large-scale, fault-tolerant, distributed systems and API microservices.
• Strong background in designing, analyzing, and improving efficiency, scalability, and stability of complex systems.
• Excellent understanding of low-level OS concepts: multi-threading, memory management, networking, and storage performance.
• Expert-level programming in one or more of: Rust, Go, Python, or TypeScript.
• Knowledge of modern LLMs and generative models and how they are served in production is a plus.
• Experience working with the open source ecosystem around inference is highly valuable; familiarity with SGLang, vLLM, or NVIDIA Dynamo will be especially handy.
• Experience with Kubernetes or container orchestration is a strong plus.
• Familiarity with GPU software stacks (CUDA, Triton, NCCL) and HPC technologies (InfiniBand, NVLink, MPI) is a plus.
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $250,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is looking for a Senior Data Engineer to help define, build, and operate the data infrastructure that handles millions of events every day to power Together’s mission-critical systems. As a Senior Data Engineer, you will work with our Data and Commerce engineering team to scale the data processing components of Together’s usage-based billing system, real-time customer-facing analytics product, and internal business intelligence tools. You will work across both cloud-native services and globally distributed data centers.

If you thrive in fast-paced environments and have a passion for defining and building early-stage data platforms for a rapidly scaling and data-intensive company, this is for you.

Requirements

• 5+ years of demonstrated experience in building large scale, fault tolerant, distributed data platforms, stream processing pipelines, ETLs, etc
• Expert-level skills in designing, building, and operating stream processing pipelines with services like AWS Kinesis, Apache Kafka, or Redpanda
• Expert-level knowledge of building real-time customer facing analytics systems using services like AWS TimeStream or Clickhouse
• Proficiency in writing and maintaining Infrastructure as Code (IaC) using tools like Terraform, AWS CDK, or Pulumi
• Proficiency in version control practices and integrating IaC with CI/CD pipelines.
• Proficiency in implementing and managing GitOps workflows with tools such as ArgoCD, Github Actions, TeamCity, or similar
• Proficiency in one or more of Golang, Rust, Python, Java, or TypeScript
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
• Experience with Kubernetes, or containers a plus

Responsibilities

• Identify, design, and develop foundational data infrastructure components capable of handling millions or billions of events daily
• Analyze and improve the robustness and scalability of existing data processing infrastructure
• Partner with product teams to understand functional requirements and deliver solutions that meet business needs
• Write clear, well-tested, and maintainable infra-as-code and software for both new and existing systems
• Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance
• Participate in an on-call rotation to address critical incidents when necessary

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $240,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

As a Senior Developer Productivity Engineer at Together AI, you’ll own the systems and tooling that empower engineers to ship high-quality software faster. You’ll optimize workflows, enhance testing, enable reliable and reusable CI/CD, and work with developers to build out stable local environments. Your work will directly impact release velocity, developer happiness, and cross-cutting enablement ensuring engineers spend less time churning on infrastructure and more time building.

Responsibilities

• Automate CI/CD pipelines for zero-downtime deployments (e.g., Canary, Blue/Green).
• Create smart pipelines encouraging reusable workflows and simplicity
• Streamline build/test/deploy workflows.
• Build shared tooling (CLIs, codegen, IDE plugins) to accelerate teams.
• Reduce friction (e.g., faster builds, hot-reload, test tooling).
• Collaborate with developers to identify pain points and streamline workflows.
• Champion best practices through documentation.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or related field or 5+ years of industry experience in DevOps/SRE, developer tooling, or infrastructure engineering.
• Proficiency in Python, Go, and JavaScript/TypeScript (for tooling, automation, and FE enablement).
• Strong experience with CI/CD tools (GitHub Actions, ArgoCD, Gitops methodology) for building scalable, reusable pipelines.
• Experience with local dev environment tooling (e.g., Skaffold) and containerized development workflows
• Experience with creating starter templates in coordination with engineering for enabling rapid spin up of new services 
• Strong ownership and a builder mindset, you love creating tools others rely on.
• Problem-solving rigor and attention to detail (e.g., diagnosing flaky tests, build latency).

Nice to have

• Kubernetes expertise (EKS, K3s) and optimizing containerized builds.
• Infrastructure as Code (Terraform, Ansible, Pulumi).
• Front-end tooling familiarity (e.g., React, Next.js, Jest) to optimize FE dev workflows.
• Monitoring/observability (Prometheus, Grafana, Honeycomb) to debug bottlenecks.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $150,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy

About the Role

Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.

We're looking for a Senior ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.

This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.

• Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.
• Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.
• Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.
• Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.
• Join a small, early-stage team with outsized impact on a fast-growing product area.

Responsibilities

• Optimize inference performance for voice models (STT, TTS, speech-to-speech) — targeting best-in-class TTFB, throughput, and GPU utilization across our curated model set.
• Productionize voice models on serverless and dedicated endpoints, including batching strategies, streaming inference, and memory management tailored to audio workloads.
• Build and maintain a voice model evaluation framework — measuring WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation accuracy for TTS.
• Enable new model architectures in our serving stack as the field evolves, including audio-native LLMs, codec-based models (SNAC), and speech-to-speech systems.
• Collaborate with model partners to integrate and optimize their models (Cartesia, Deepgram, Rime, and others) running on Together's infrastructure.
• Profile and debug performance across the full inference stack — from GPU kernels to framework-level bottlenecks — and ship measurable improvements.
• Work with the platform engineering side of the team to ensure the serving layer meets the latency and reliability requirements of real-time voice APIs.
• Contribute to voice model fine-tuning capabilities (STT and TTS) as we enable customers to build differentiated voice experiences on Together.
• Lay the groundwork for multiple new products down the line.

Requirements

• 5+ years of experience in ML engineering, with a focus on model serving, inference optimization, or ML infrastructure.
• Hands-on experience with LLM serving engines (vLLM, SGLang, TensorRT-LLM, or similar) — comfortable reading and modifying engine internals, not just using APIs.
• Strong proficiency in Python and PyTorch; experience with GPU profiling and optimization (CUDA, memory management, kernel-level debugging).
• Track record of shipping ML systems to production with measurable performance improvements.
• Strong product sense — you think about what developers building voice apps actually need, not just what's technically interesting.
• Comfort working on a small, early-stage team where you'll wear multiple hats and move fast.
• Experience with speech and audio ML (ASR, TTS architectures, audio signal processing) is a strong plus but not required — you can learn this quickly if you have strong ML engineering fundamentals.
• Familiarity with audio codecs and tokenization schemes (SNAC, Encodec, DAC) is a plus.
• Experience training or fine-tuning speech models is a plus.
• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field, or equivalent practical experience

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $260,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

As a Senior Network Engineer at Together, you are responsible for designing, implementing, and maintaining our network infrastructure to ensure seamless connectivity and optimal performance for all user-facing services and production systems. As both a strategic planner and a hands-on engineer, you apply sound networking principles, operational discipline, and advanced automation to our network environments.

You specialize in networking systems—including routing, switching, network security, and protocols—implementing best practices for availability, reliability, and scalability. You have a keen interest in network design, optimization, and emerging technologies in HPC-based data center networking.

Outstanding problem-solving abilities and a comprehensive understanding of fundamental network theory are also critical to your success.

Requirements

• 8+ years of professional experience building, managing, and supporting large-scale hybrid data center networks (excluding enterprise networks).
• High level of proficiency with TCP/IP networking architecture and technologies such as BGP, OSPF, VXLAN, EVPN, and QoS.
• Experience developing network automation pipelines using Python, Ansible, or other languages/tools utilized in infrastructure automation.
• Proficient in using tools such as Wireshark, tcpdump, nmap, MTR, and curl to identify connectivity issues, latency problems, and network bottlenecks.
• Experience designing and supporting multi-tenant networks
• Hands-on experience deploying and supporting network devices from Cisco, Arista, Juniper, and Mellanox.
• Experience working with cloud networks such as AWS, GCP, and Azure.
• Solid experience working in and troubleshooting within a Linux environment.

Responsibilities

• Design, deploy, manage and maintain global multi-vendor, multi-protocol high performance compute networks.
• Analyze data to diagnose and identify root causes to network issues to minimize downtime
• Evaluate and recommend network technologies, hardware, and software solutions.

• Participate in design reviews to ensure the proposed network architecture aligns with business needs and is optimized for performance, scalability, and reliability. 
• Manage relationships with external vendors and partners to test and verify hardware and software selections.

• Develop, and deploy systems and tools to keep all networks running reliably and efficiently
• Establish and implement industry best practices and contribute to the design of new scalable network solutions
• Ensure compliance with IT governance standards and best practices.
• Lead projects to address complex technical challenges, directly contributing to roadmaps and partner alongside the best engineers in the industry to develop world-class solutions

Preferred

• Knowledge of RoCE and Infiniband protocols a plus
• Experience with Docker, Kubernetes, or Slurm a plus 
• Understanding of AI training workloads and the demands they exert on networks a plus

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $190,000 - $250,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.

We're looking for a Senior Platform Engineer to own the API and infrastructure layer for voice workloads. You'll build the real-time WebSocket and HTTP APIs that developers use to ship voice experiences, design autoscaling for latency-sensitive streaming workloads, and ensure our multi-provider voice platform is reliable enough for production voice agents handling millions of calls.

This is a foundational hire on a small, high-impact team. Voice APIs have fundamentally different infrastructure requirements than text-based inference — bidirectional audio streaming, stateful connections, tight latency SLOs, and complex multi-model routing. You'll define how developers interact with Together's voice platform as we grow from early customers to the default infrastructure for voice AI.

• Own the real-time API layer (WebSocket + HTTP streaming) that powers Together's voice platform.
• Design autoscaling and orchestration for voice workloads running on tens of thousands of GPUs.
• Build the developer experience — APIs, observability, and tooling — for a fast-growing product area.
• Work with production voice customers (contact centers, AI agents, communication platforms) to ship what they actually need.
• Join a small, early-stage team with outsized impact on a new product line.

Responsibilities

• Build and harden real-time WebSocket and HTTP streaming APIs for STT and TTS — including connection lifecycle management, backpressure, error handling, and reconnection, at the reliability bar needed for production voice agents.
• Design and ship autoscaling for voice model endpoints that handles bursty, real-time traffic patterns — accounting for concurrent connection limits, streaming state, and hard latency ceilings.
• Implement voice-specific API features: word-level alignment, speaker diarization in realtime, audio format flexibility (g711/mulaw for telephony, PCM, WebRTC formats), pronunciation controls, and multi-context WebSocket support.
• Build voice-specific observability — latency breakdowns, audio quality signals, and dashboards that help both the team and customers debug issues.
• Own multi-model normalization across our model partners (Cartesia, Deepgram, Rime, and others), ensuring consistent API behavior regardless of the underlying provider.
• Collaborate with the ML engineering side of the team on the interface between the API layer and the model serving stack, ensuring latency and reliability requirements are met end-to-end.
• Contribute to developer experience — API design, documentation, integration cookbooks, playground and showcasing how best-in-class voice agents are built.
• Lay the groundwork for multiple new products down the line.

Requirements

• 5+ years of experience building large-scale, real-time distributed systems and API services.
• Deep expertise in real-time streaming infrastructure — WebSocket server architecture, Server-Sent Events, bidirectional streaming, connection multiplexing, and stateful protocol design.
• Expert-level programming in TypeScript and Python; experience with Rust is a plus.
• Strong distributed systems fundamentals: load balancing, autoscaling, rate limiting, and traffic shaping for latency-sensitive workloads.
• Experience with Kubernetes — including custom autoscalers, resource management, and health checking for stateful services.
• Strong product sense — you care about API ergonomics and think about what developers building voice apps actually need.
• Comfort working on a small, early-stage team where you'll wear multiple hats and move fast.
• Experience with audio or media protocols (WebRTC, g711, PCM encoding) is a strong plus.
• Familiarity with ML model serving infrastructure and how inference engines work is a plus — you'll interface with the serving layer regularly.
• Full-stack experience (React, Next.js) is a nice-to-have for contributing to developer-facing tooling.
• Bachelor's or Master's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $260,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

As a Solutions Architect at Together AI, you will work with customers and prospects to create business value through Generative AI applications. Solutions Architects at Together are trusted advisors to our customers that evaluate, identify and demonstrate how Together can solve their AI needs. As key contributors to our sales organization, Solution Engineers add tremendous value to the customer journey and directly impact company growth and revenue. This is an exciting opportunity for a deeply technical professional passionate about AI and customer success to make a significant impact in a fast-paced, innovative environment.

Responsibilities

• Act as a technical advisor to our most strategic customers, deeply embedding with them to support the ideation and development of innovative applications using OSS models on Together AI
• Run complex demonstrations and POCs of Together’s entire stack, including both hardware and software solutions
• Collaborate with sales to qualify new prospects and support existing customers along their journey to build cutting-edge Generative AI solutions
• Build and maintain strong relationships with customer leadership and stakeholders, ensuring the successful deployment and scaling of their applications
• Deliver high-value feedback to our Product, Engineering, and Research teams, ensuring our platform continues to evolve to meet customer needs
• Build educational content and tooling for both internal and external use around Together’s solutions (i.e., playbooks, blogs, demos, etc.)

Qualifications

• 5+ years of experience in a customer-facing technical role with at least 2 years in a pre-sales function 
• Excellent communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Ability to consult with new and existing customers to map business needs to technical solutions
• Strong technical background, with knowledge of AI, ML, GPU technologies and their integration into high-performance computing (HPC) environments.
• Strong understanding of training, fine-tuning and inference in the context of open source LLMs
• Proficiency in Python and JavaScript, with experience building and delivering prototypes on API platforms.
• Familiarity with infrastructure services (e.g., Kubernetes, SLURM), infrastructure as code solutions (e.g., Ansible), container infrastructure Docker), and scripting and programming languages (Python, Javascript)
• Strong sense of ownership and willingness to learn new skills to ensure both team and customer success.
• Ability to operate in dynamic environments, adept at managing multiple projects, and comfortable with frequent context switching and prioritization.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. 

Compensation

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US salary range for this full-time position is: $180-260K OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our Privacy Policy at https://www.together.ai/privacy

About the Role

Together AI is building high-performance AI inference infrastructure and the software platform around it. We're looking for a senior Analytics Engineer who sits at the intersection of data engineering and business intelligence — someone who can turn raw, complex data into clean, trusted, well-documented models that the whole company can reason from.

You'll own the transformation layer of our data warehouse: shaping bronze/silver/gold models, designing dimensional schemas, and acting as the connective tissue between engineering systems and business stakeholders. You are equally comfortable deep in a dbt project and in a room with Finance, GTM, and Product aligning on definitions.

Requirements

• Expert SQL: window functions, complex aggregations, query optimization, cost-aware pattern selection, proficiency in Snowflake or equivalent cloud warehouse.
• dbt: deep, production-grade experience — models, tests (singular + generic), docs, snapshots, macros, packages, and incremental strategies. You've designed a dbt project from scratch and maintained it in production.
• Airflow / Astronomer: production DAG authoring, backfill handling, reliability patterns, and the Cosmos dbt integration.
• Dimensional modeling: you've read Kimball (or absorbed the equivalent), know the difference between star and snowflake schemas by feel, understand slowly changing dimensions, and can explain why a fact table's grain matters.
• Stakeholder management: demonstrated experience partnering with non-technical stakeholders, driving metric alignment, and delivering trusted data products — not just pipelines.
• Strong written communication: your documentation and async updates are clear enough that people don't need to ask follow-up questions.

Strong plus

• Experience with financial data or billing data — ARR, usage-based billing, invoice reconciliation, revenue recognition patterns. We operate a usage-based inference billing system and this context transfers directly.
• Experience with PII handling, data masking, access-tier modeling, or compliance work (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
• Experience with Hex, Metabase, or similar notebook/BI tooling that sits on top of your dbt models.
• Prior experience in a high-growth AI/ML infrastructure or platform company.

Responsibilities

Modeling & transformation

• Own and evolve the dbt transformation layer: design, implement, test, document, and maintain modular dbt projects that cover billing, product usage, financial data, and operational metrics.
• Build analytics-ready dimensional models following Kimball methodology: star schemas, conformed dimensions, fact tables with the right grain, and SCD Type 2 for slowly changing entities.
• Design for correctness, performance, and cost — partition strategies, incremental models, and avoiding full-table scans.
• Build and maintain a semantic/metrics layer with consistent, auditable metric definitions reused across notebooks, BI, and APIs.

Pipeline orchestration

• Author and maintain Airflow DAGs (Astronomer-managed) that orchestrate dbt runs, data quality checks, and downstream dependencies reliably.
• Apply solid DAG design: idempotent tasks, proper backfill strategies, SLA alerting, and clean dependency graphs.
• Work in our Cosmos (dbt + Airflow) integration — you know when to use a DbtTaskGroup vs a custom operator.

Data quality & governance

• Implement data quality checks at every layer: freshness, null/uniqueness tests, referential integrity, distribution drift, and business-rule assertions.
• Drive data stewardship practices: ownership, SLAs, clear "source of truth" definitions, and change communication.
• Handle PII fields correctly — masking, anonymization, and access-tier alignment. Compliance experience (SOC 2, ISO 27001, or similar) is a meaningful plus.

Stakeholder management

• Be the analytical partner to Finance, GTM, Product, and Engineering — translate business questions into durable data models, not one-off queries.
• Drive alignment on metric definitions, data ownership, and delivery tradeoffs across stakeholders with competing priorities.
• Communicate data quality issues, model changes, and breaking changes proactively. You treat downstream consumers as customers.
• Write clear documentation — model descriptions, column-level lineage, and business context — so analysts can self-serve with confidence.
• Run or contribute to data reviews, data-driven planning discussions, and architecture reviews involving the warehouse.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $240,000 - $275,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is seeking a Staff Backend Engineer to own the technical vision, architecture, and execution of the commerce platform powering Together's Cloud products. As a staff engineer on the Commerce Engineering team, you will set the engineering direction for mission-critical capabilities — including usage-based billing, payment processing, customer-facing analytics, and product entitlements — while raising the bar for the engineers around you.

This role is ideal for a seasoned engineer who can operate at multiple altitudes: driving system design and long-term architecture decisions while staying hands-on in code. You'll be the connective tissue between engineering, product, finance, and go-to-market — translating complex business requirements into durable, scalable backend primitives that directly impact revenue and customer experience. You'll be expected to define not just what we build, but how we build it well.

Requirements

• 8+ years of experience building large-scale, fault-tolerant API-driven services and backend systems
• Demonstrated track record of tech lead or staff-level scope — owning system architecture decisions, influencing team direction, and delivering multi-quarter initiatives
• Deep expertise in designing relational database schemas and backend APIs that support complex product and business requirements at scale
• Strong experience leading and influencing cross-functional teams — comfortable partnering with product, finance, and non-technical stakeholders
• Expert-level proficiency in one or more of Golang, TypeScript, Python, C++, or Java
• Deep knowledge of distributed systems tradeoffs: consistency, availability, fault tolerance, and performance at scale
• Strong understanding of low-level systems concepts: multi-threading, memory management, networking, storage, and I/O
• Experience with infrastructure as code (Terraform, AWS CDK, Pulumi) and mature CI/CD workflows
• Experience with payment processors (e.g. Stripe) and billing systems strongly preferred
• Experience with Kubernetes and containerized deployments a plus
• Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent practical experience

Responsibilities

• Define and own the technical roadmap for Together's monetization and billing pipeline infrastructure, spanning usage metering, contract billing, entitlements, spend governance, and payments processing
• Lead architecture reviews for major systems investments, ensuring long-term scalability, correctness, and maintainability across the commerce stack
• Drive cross-functional alignment with Product, Finance, and Go-To-Market teams — translating business strategy into clear engineering requirements and system designs
• Mentor and grow engineers on the team through design feedback, code reviews, technical coaching, and setting high standards for craft
• Own data modeling and schema design for core commerce entities, making principled tradeoffs between flexibility, performance, and correctness
• Establish and evolve API contracts in collaboration with client engineering teams, enabling high-quality customer-facing product experiences
• Champion reliability and scalability — lead incident response, drive postmortems, and systematically improve the resilience of commerce APIs and distributed systems
• Identify and reduce technical debt proactively, balancing pragmatic delivery with sustainable engineering practices

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $230,000 - $270,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy

About the Role

Together AI is building high-performance inference compute and the software platform around it. We’re looking for a Staff Data Warehouse Engineer with strong fundamentals and high growth potential to evolve into a technical lead. You’ll design and operate the warehouse from bronze → silver → gold, own core data models and metrics, and raise the bar on data quality and governance across the org.

Responsibilities

• Architect and operate a medallion/curated data warehouse stack (bronze/silver/gold) for product, usage, billing, and operational data.
• Build and maintain Airflow orchestrated pipelines and dbt transformation projects (modular, tested, documented).
• Design analytics-ready models: SCD Type 2, star schemas, and appropriate normalization for upstream canonical layers.
• Lead Master Data Management (MDM) patterns (golden records, reference data, deduping, identity resolution).
• Implement and automate data quality checks (freshness, nulls, referential integrity, distribution drift, anomaly detection).
• Establish data governance habits: data stewardship, ownership, SLAs, and clear definitions for “source of truth.”
• Build and maintain a business semantic layer (consistent metric definitions, dimensions, and reusable logic) used by notebooks/BI.
• Partner with stakeholders (Product, Engineering, Finance, GTM, Ops) to translate questions into durable datasets and metrics.
• Use SQL, Python, and Spark where scale demands it; optimize for correctness, performance, and cost.
• Mentor engineers and contribute to standards (code review, design docs, runbooks), paving the path to tech lead.

Requirements

• Strong warehouse fundamentals and production experience delivering trusted datasets and metrics.
• Expert SQL (window functions, dimensional modeling, performance tuning).
• Hands-on with dbt (models, tests, docs, snapshots, macros) and Airflow (DAG design, backfills, reliability).
• Solid Python for data tooling and automation; experience with Spark (PySpark/SQL) is a plus.
• Practical experience with SCD2, star schemas, and handling slowly changing business entities.
• Strong stakeholder management: you can drive alignment on definitions, tradeoffs, and delivery timelines.
• High standards for data quality, reliability, and maintainability.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $240,000 - $275,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy  

Staff Engineer, Customer Insights

About the role

Together AI is seeking an experienced Staff Engineer to help found, build, and scale Customer Insights, the team responsible for the customer-facing visibility layer of Together’s Cloud. This role will shape how customers understand their activity, investigate what happened, respond when something needs attention, and govern their AI workloads with confidence.

In the near term, you will turn today’s fragmented visibility patterns into coherent product and platform foundations: historical analytics, activity history, audit logs, event timelines, notifications, and investigation workflows. You will partner closely with Together’s cloud product engineering teams and data platform team to make high-quality customer visibility a built-in capability across Together Cloud rather than a bespoke effort for each surface. Longer term, you will shape how these foundations evolve beyond dashboards and static views into AI-first investigation and insight workflows: systems that can summarize activity, explain anomalies, correlate events across surfaces, recommend actions, and provide trustworthy context to both human operators and autonomous agents.

This is a deeply hands-on role for an engineer who enjoys critical-path product and platform work. You will design the event, query, delivery, and governance systems behind customer insights while also building the user-facing workflows that help enterprise customers answer: what happened, who acted, when it happened, what matters now, and whether they need to act.

Responsibilities

• Design and drive the architecture for Together's Customer Insights platform, including customer-visible events, activity history, audit logs, timelines, notifications, and historical analytics and dashboards views.
• Build and operate the shared event and historical analytics visibility layer that helps product teams define important customer-facing events once and surface them consistently across analytics, logs, timelines, notifications, and governance views.
• Write and maintain critical-path backend and product code used by multiple teams and customer-facing surfaces.
• Partner with Data Platform and Observability engineering teams to leverage Together’s core data stack and capabilities to build out Customers Insights data collection and processing systems.
• Create customer workflows that connect summaries, timelines, evidence, actors, related objects, and next steps so users can investigate operational questions without relying on support or ad hoc analysis.
• Establish standards and review patterns for which events, evidence trails, notification hooks, and governance signals must exist before new product capabilities launch.
• Define reliability, freshness, completeness, and correctness measures for customer-facing visibility systems, then build the checks and operating loops needed to maintain trust at scale.
• Mentor engineers, influence architectural direction across teams, and help define the hiring bar as the Customer Insights area grows.
•  

Requirements

• 8+ years of experience building and operating large-scale distributed systems, product platforms, or customer-facing backend systems in production environments.
• Proven experience designing platforms or product systems used by multiple engineering teams or enterprise customers.
• Strong backend engineering skills in one or more of TypeScript, Go, Python, Java, C++, or similar production languages.
• Deep experience with event-driven systems, audit trails, activity feeds, analytics platforms, observability systems, notification systems, or investigation workflows.
• Strong data modeling and systems design judgment, including schemas, retention, indexing, query performance, access control, and correctness tradeoffs.
• Demonstrated ability to turn ambiguous customer and operational needs into clear technical architecture, incremental delivery plans, and durable product foundations.
• Experience partnering across product, infrastructure, data, security, and customer-facing teams to land cross-cutting architecture without relying on formal authority.
• Comfort writing critical production code while also setting technical direction, mentoring senior engineers, and raising engineering standards.
• Bachelor's or Master's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.

Preferred Qualifications

• Experience building enterprise SaaS audit logs, governance tooling, compliance evidence systems, data lineage, or incident investigation products.
• Experience with high-volume event pipelines, streaming systems, OLAP or time-series storage, search systems, or large-scale analytical query engines.
• Experience building notification, alert routing, escalation, or workflow automation systems.
• Experience designing customer-facing analytics, reporting, dashboards, or operational intelligence products.
• Experience with multi-tenant cloud platforms, identity-aware access patterns, sensitive data handling, and retention controls.
• Experience with AI infrastructure, developer platforms, observability, or systems that support mission-critical enterprise workloads.
• Experience running production workloads on Kubernetes or cloud infrastructure and using Infrastructure as Code and modern CI/CD workflows.
• Experience building product surfaces in addition to backend platforms, especially where UX quality and technical correctness both matter.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200k-270k+ equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an 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.

Please see our privacy policy at https://www.together.ai/privacy.  

About the role

As a Dedicated AI Factory TAM at Together AI, you will serve as the named technical owner for one of our most strategic enterprise relationships. You will be the primary technical point of contact across all infrastructure domains — compute, networking, storage, and facilities — ensuring smooth delivery and operational health of large-scale GPU deployments. This role sits at the intersection of deep infrastructure expertise and high-stakes customer partnership, making you a critical driver of both customer success and company growth.

Responsibilities

• Serve as the named technical point of contact for a dedicated strategic customer, owning the end-to-end technical relationship across compute, networking, storage, and facilities
• Drive structured engagement through regular cadences including status reporting, technical steering meetings, and executive business reviews
• Translate customer operational feedback into actionable input for Engineering, Product, and Infrastructure roadmaps
• Lead issue lifecycle management, escalation, and RCA authorship across all infrastructure domains in partnership with Support, SRE, DC Ops, and Engineering teams
• Own end-to-end RMA coordination and hardware lifecycle management, including acceptance testing, spare inventory management, and hardware health reporting for large-scale GPU deployments
• Maintain deep technical expertise across the customer's infrastructure stack — GPU compute, high-speed fabric, and large-scale storage systems — advising on configuration, operational best practices, and incident resolution
• Own the observability strategy for the customer estate, including alert policy definition, dashboard development, and proactive health management across all infrastructure layers
• Coordinate DC operations and facilities events in partnership with internal teams and hosting providers, ensuring SLA compliance and cluster availability
• Act as project manager for all capacity expansions, owning the full node deployment lifecycle from freight receipt through production acceptance

Qualifications

• 5+ years in a customer-facing technical role, with 2+ years in dedicated technical account management or solutions architecture for large-scale AI or HPC infrastructure
• Deep expertise in GPU infrastructure — GPU health diagnostics, RMA workflows, and hardware acceptance testing
• Hands-on experience with large-scale Ethernet and InfiniBand fabric architecture
• Working knowledge of enterprise storage systems, including high-density NVMe, parallel file systems, and metadata infrastructure
• Experience with DC operations, facilities coordination, and hosting provider SLA management
• Strong ownership mindset for incident management, RCA authorship, and executive-level customer communication
• Proficiency in infrastructure monitoring and observability tooling (Prometheus, Grafana, or equivalent)
• Proven ability to manage multiple concurrent workstreams with hyperscaler-level rigor and communication standards
• Proficiency in Python, Bash, or infrastructure automation tools preferred

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers on our journey in building the next generation of AI infrastructure. 

Compensation

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $260-290K OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. 

Location

San Francisco, CA (Hybrid) or New York, NY (Hybrid)

Equal Opportunity

Together AI is an 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.

Please see our Privacy Policy at https://www.together.ai/privacy

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.

About this role: 

As a Senior Data Scientist for Go-to-Market at fal, you will be the analytical backbone of our revenue organization. Embedded directly with the GTM function, you will own the metrics that tell us how our pipeline is performing, how our sales team is executing, and where the next dollar of revenue is most likely to come from.

This is a high-leverage, high-visibility role. GTM leadership will look to you for the answers - on rep performance, quota attainment, pipeline health, AM coverage, and segment economics - and you'll shape both the questions we ask and the systems we build to answer them. You'll partner closely with Product Intelligence and Data Engineering as part of fal's center-of-excellence data team, while acting as the embedded specialist for everything GTM.

Key responsibilities:

• Own the metrics and reporting that drive sales execution at fal: pipeline health, quota attainment, conversion rates, sales cycle, AM coverage, and segment-level economics.
• Partner directly with the sales leadership and rev-ops to translate strategic questions into measurement frameworks and weekly operating cadences.
• Build the data foundations that make GTM analytics first-class: account-manager tracking, territory and quota models, opportunity-stage instrumentation, and rep-level performance views.
• Run rigorous deep-dives - win/loss, lead source effectiveness, segmentation, expansion drivers - that change how GTM allocates time and headcount.
• Shape the CRM, internal tooling, and downstream data models that everyone in GTM depends on, and set the standards for how GTM data is captured, joined, and trusted.

Requirements:

• 5+ years of experience in data science or analytics roles, with at least 3+ years specifically in GTM, RevOps, or sales analytics at an enterprise or B2B SaaS company. This is a hard requirement - we are looking for someone who has seen many variations of this problem and can run with it.
• Advanced SQL and proficiency in Python for analytics, modeling, and experimentation.
• Deep familiarity with Salesforce data, pipeline mechanics, quota and territory models, and the operational rhythms of an enterprise sales org.
• Strong proficiency with dbt and modern analytics stacks; comfort partnering with data engineering on production-grade pipelines.
• Proven ability to operate as an embedded analytics partner - building trust with sales leadership and translating ambiguous strategic asks into clear measurement.
• Demonstrated bias for action; you set up the dashboard, write the SQL, and ship the framework yourself when needed.
• A track record of shipping data products that change behavior, not just dashboards that get viewed once.

Nice to have:

• Experience supporting both self-serve/PLG and enterprise sales motions.
• Experience working on developer-facing or API products.
• Familiarity with usage-based pricing and consumption-driven sales motions.
• Early-stage or fast-scaling environment experience.

Compensation

• $180,000-225,000 plus equity + benefits (This range is across 2 levles Senior and Staff)

Location

• San Francisco, CA (willing to consider remote for Senior and Staff levels)

What we offer at fal

• Interesting and challenging work

• A lot of learning and growth opportunities

• We are currently hiring in downtown San Francisco.

• We offer relocation assistance to San Francisco.

• Health, dental, and vision insurance (US)

• Regular team events and offsites

• Help design, train, with a focus towards deploying onboard machine learning models that improve the performance/latency of the Kodiak autonomy stack which includes quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling.
• Help identify and achieve parity between onboard and deployed offboard models using validation on HIL 
• Collaborate with cross-functional teams including perception, planning, simulation, and autonomy infrastructure to integrate ML models into production systems.
• Analyze real-world driving logs to identify edge cases and improve model robustness and safety.
• Use profiling tooling to additionally help improve the train time for models by identifying and removing bottlenecks.
• Contribute to the development of scalable AI infrastructure that supports continuous learning and deployment across Kodiak’s fleet.

• MS or PhD in Computer Science, Robotics, Machine Learning, or a related technical field, or equivalent practical experience.
• 6+ years experience deploying machine learning or deep learning systems in production environments.
• Proficiency in Python, C++ and modern ML frameworks such as PyTorch or TensorFlow.
• Experience working with large-scale distributed training systems.
• Solid understanding of optimization, and machine learning fundamentals.
• Strong software engineering fundamentals including testing, debugging, and system design.
• Ability to work collaboratively across teams to solve complex autonomy and robotics challenges.

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Long Term Disability, Short Term Disability, Life Insurance
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA
• Various incentive programs (referral bonuses, patent bonuses, etc.)

What We're Looking For

As a Machine Learning Solutions Engineer, you will operate at the intersection of machine learning, distributed systems, and cloud infrastructure.
You will partner with customers to design and deploy end-to-end AI systems, spanning:

• Model development and training
• GPU infrastructure and cluster design
• Distributed inference and production deployment

This role goes beyond traditional ML solutions engineering—you will act as a technical architect, helping customers make critical decisions across compute, orchestration, and system design.

The role can be based out of our New York City or San Francisco office, with an in-office requirement of at least 2-3 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time. The annual base pay range for this role is $150,000 - $195,000, in addition to a variable pay component and meaningful equity. 

What You’ll Do

Customer Architecture & Technical Leadership

• Partner with customers to understand ML workloads, infrastructure constraints, and scaling requirements
  
  
• Architect end-to-end solutions across:
  
  
• Data pipelines (CPU → GPU workflows)
  
  
• Distributed training (multi-node, multi-GPU)
  
  
• High-throughput inference systems
  
  
• Translate business goals (latency, cost, throughput) into technical system design decisions
  
  

GPU & Infrastructure Design

• Design and optimize workloads across GPU clusters (H100, H200, B200, etc.)
  
  
• Advise on:
  
  
• Training vs inference cluster design
  
  
• Interconnect choices (Ethernet vs Infiniband / RDMA vs Roce)
  
  
• Storage strategies (local NVMe vs networked / object storage)
  
  
• Model and optimize for:
  
  
• Tokens/sec, tokens/$
  
  
• Throughput vs latency tradeoffs
  
  
• GPU utilization and scheduling efficiency
  
  

Kubernetes & Platform Systems

• Design and support deployments on Kubernetes (EKS, GKE, on-prem clusters)
  
  
• Work with:
  
  
• GPU scheduling (time-slicing, MIG, bin-packing)
  
  
• Autoscaling and workload orchestration
  
  
• Helm-based deployments and multi-tenant environments
  
  
• Help customers balance:
  
  
• Raw Kubernetes flexibility vs platform abstraction (Lightning)
  
  

Demos, POCs, and Execution

• Build and deliver technical demos and POCs that showcase:
  
  
• Distributed training workflows
  
  
• Scalable inference endpoints
  
  
• End-to-end ML pipelines on Lightning AI
  
  
• Scope and lead POCs aligned to customer success metrics (latency, cost, reliability)
  
  

Cross-Functional Impact

• Act as the bridge between customers, product, and engineering
  
  
• Provide feedback on:
  
  
• Platform gaps in infrastructure, orchestration, and performance
  
  
• Emerging patterns in GPU usage and distributed systems
  
  
• Influence roadmap across ML workflows and infrastructure capabilities
  
  

Enablement & Thought Leadership

• Create technical content
• Architecture guides (e.g., high-throughput LLM inference systems)
• Best practices for GPU utilization and scaling
• Educate customers on modern AI infrastructure patterns

What You’ll Need

ML + Systems Expertise

• 3–6+ years experience in:
  
  
• Machine Learning / AI Engineering
  
  
• Solutions Engineering / Sales Engineering / ML Consulting
  
  
• Strong understanding of:
  
  
• Training vs inference workloads
  
  
• Model optimization (quantization, batching, caching, etc.)
  
  

GPU & Distributed Systems

• Experience working with:
  
  
• GPU clusters (NVIDIA stack preferred)
  
  
• Distributed training or inference systems
  
  
• Familiarity with:
  
  
• NCCL, CUDA, or GPU performance profiling
  
  
• Networking concepts (RDMA, Roce, Infiniband, high-throughput systems)
  
  

Kubernetes & Cloud Platforms

• Hands-on experience with:
  
  
• Kubernetes (EKS, GKE, or on-prem)
• Slurm 
• Containerization (Docker)
  
  
• Exposure to:
  
  
• GPU scheduling in Kubernetes environments
  
  
• Multi-tenant or production ML deployments
  
  

Programming & Tooling

• Strong Python skills (PyTorch preferred)
  
  
• Experience building:
  
  
• ML pipelines
  
  
• APIs or inference services
  
  
• Familiarity with Lightning AI, PyTorch Lightning, or similar frameworks is a plus
  
  

Customer-Facing Excellence

• • Ability to:
    
    
    • Explain complex infrastructure and ML tradeoffs clearly
      
      
    • Run technical discovery and uncover quantifiable success metrics
      
      
  • Experience working cross-functionally with:
    
    
    • Sales, product, and engineering teams