Performance Modeling Engineer 2 in San Francisco

About the Team OpenAI’s Strategic Finance organization provides financial insights and guidance to support the company’s long-term strategy and ambitious growth. We work across Partnerships, GTM, Product, Compute, and Operations to allocate and deploy resources to the highest-impact opportunities while protecting sustainable unit economics. The B2B Strategic Finance team focuses on the financial performance of our B2B products and GTM functions, ensuring tight alignment between financial objectives and company strategy. We: Drive operational planning, financial forecasting, and performance management for our B2B business, Provide analytically-grounded insights on B2B product and financial performance to inform strategic resource allocation, Build the “0→1” financial foundations required to scale and accelerate growth. Within B2B, Partnerships are a critical lever to accelerate growth. You will help shape this business and drive successful outcomes from it. About the Role This Strategic Finance Partnerships hire will own our B2B Partnerships P&L and help drive strategic decision-making. You will be a critical finance partner to our Partnership teams, deal leads, Product owners, and Operations teams, responsible for pricing, structuring and driving success from partnerships, translating opportunities into decision- and exec-ready economics, and managing the overall Partnerships book of business. You will shape how OpenAI approaches Partnerships revenue targets, unit economics, investments, measurement, and prioritization. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Help manage the overall Partnerships business at a strategic priority, top-line impact, and resourcing / investment level. Own the financial evaluation of new B2B partnerships, modeling revenue, contribution margin, compute costs, cash flow, investment needs, and risk. Driv

About the Team OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. The Consumer Devices team is building a new generation of AI-powered products that seamlessly integrate hardware and software to create intuitive, transformative experiences. We bring together experts across embedded systems, machine learning, hardware, design, and product engineering to develop products at the intersection of AI and consumer technology. About the Role OpenAI is seeking a System Performance Engineer to profile, benchmark, and optimize performance across our embedded hardware products. In this role, you will work across operating systems, applications, camera and vision, graphics, and platform teams to define product KPIs, build performance tooling, and drive optimizations from early lab characterization through product launch and real-world usage. You will help establish the performance standards that shape the user experience of our products, ensuring they remain responsive, efficient, and reliable throughout their lifecycle. This role requires deep expertise in embedded or high-performance systems, strong operating systems fundamentals, and hands-on experience debugging under tight latency, power, and memory constraints. This role is based in San Francisco, CA. We use a hybrid work model of four days per week in the office and one day working remotely. Relocation assistance is available for new hires. In this role, you will: Develop system performance benchmarks, methodologies, and policies to evaluate end-to-end product behavior. Profile and analyze performance across key product use cases and workloads using custom and industry-standard profiling tools. Partner closely with engineering teams to identify bottlenecks and drive performance optimizations across the software stack. Define high-level product KPIs and establish measurement frameworks to measure launch readiness and monitor performance throughout the product lifecycle. Measure, re

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale. You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Profil

About the Team We bring OpenAI's technology to the world through products like ChatGPT and the OpenAI API. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role OpenAI is looking for an experienced Performance Engineer to help us scale the performance, reliability, and efficiency of our systems. In this role, you'll apply deep technical expertise to optimize infrastructure and application-level performance across mission-critical products like ChatGPT and our developer API. You’ll work cross-functionally with teams building core services, training models, and developing real-time user experiences to push our latency, throughput, and cost-efficiency to the next level. We are looking for engineers who thrive in ambiguous environments, value deep systems understanding, and are motivated by delivering measurable impact. This is a highly technical, individual contributor role focused on root-cause analysis, profiling, instrumentation, and architecture-level performance improvements across our stack. In this role, you will: Analyze and optimize performance across application, middleware, runtime, and infrastructure layers—networking, storage, Python runtime, GPU utilization, and beyond. Develop tooling and metrics that provide deep observability into system performance. Collaborate closely with infra, platform, training, and product teams to identify key performance goals and drive systemic improvements. Influence architecture and design decisions to prioritize latency, throughput, and efficiency at scale. Lead investigations into high-impact performance regressions or scalability issues in production. Drive performance testing strategies and help define SLAs/SLOs around latency and throughput for critical systems. You might thrive in this role if you: Have 7+ years of experience in software engineering with a strong tr

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE The Model Performance organization at Baseten is looking to hire our first Technical Program Manager. This is a zero-to-one role in a team that is responsible for building the core algorithms and methods that power Baseten’s high performance inference stack. You won't inherit an existing program framework, you'll build one from the ground up: the planning structure, execution processes, metrics and the cross-functional alignment that a fast-growing organization needs. Your contributions will directly impact how fast our performance R&D gets productized. If you can drive turning a set of ambitious but loosely defined initiatives into a predictable, well-governed program, this role is for you. EXAMPLE INITIATIVES Take a look at these blog posts written by members of our Model Performance team: How to build a day-0 API for Kimi K3 How we built the new fastest API for GLM-5.2 Inference engineering for DeepSeek V4 Pro 0813 RESPONSIBILITIES Own execution across Model Performance's active project portfolio, freeing the team's technical leads to focus on technical direction rather than tracking. Design and stand up the planning structures, operating cadences, and status reporting mechanisms that best fits the team’s DNA. Coordinate model release and optimization programs end to end, including day-zero launches, sequencing the work across performance engineering, infra, and release stakeholders. Drive cross-team al

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Are you passionate about advancing the application of artificial intelligence? We are looking for a Software Engineer focused on ML performance to join our dynamic team. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM Inference. If you are a backend engineer who thrives on making things faster and is excited about open-source ML models, we look forward to your application. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Model Performance team: Baseten Embeddings Inference: The fastest embeddings solution available The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure. Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vllm, sglang, CUDA, and other libraries to debug ML performance issues. Apply and scale optimization techniques across a wide range of ML models, particularly large language models. Collaborate with a diverse team to design and implement innovative solutions. Own projects from idea to production. REQUIREMENTS Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. Experience with one

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE OPPORTUNITY We are looking for Senior Software Engineers to join our team. This is a specialized, high-impact role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will not just be building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure; you will be defining the roadmap, driving key technical decisions, and taking full ownership of the future of this work. RESPONSIBILITIES Benchmarking : Evaluate, run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse, disaggregated serving). DevEx Improvement : Develop and maintain internal GPU-enabled development environments (similar to GitHub Codespaces). You will ensure the team has seamless, high-performance "dev machines" optimized for model experimentation. Tool Development : Build and contribute to open-source tools such as InferenceMAX and genai-bench to automate model evaluation, benchmarking and analysis. System Profiling : Use profilers like PyTorch Profiler, NVIDIA Nsight Systems and py-spy to collect performance profiles, identify bottlenecks, and debug the compute/networking stack. Monitoring & Observability : Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance. Continuous Integration : Auto

About the Team We’re hiring software engineers to make OpenAI’s Model Performance teams more productive. These teams work on the systems, tooling, and infrastructure that help improve model performance across OpenAI’s training and inference workloads at frontier scale. About the Role We’re looking for an autonomous, high-ownership developer productivity engineer who cares deeply about helping other engineers move faster, safer, and with more confidence. This role will sit within OpenAI’s Model Performance organization, contributing to developer infrastructure, CI systems, testing workflows, tooling, and broader performance infrastructure efforts. There is also a strong opportunity to contribute to the Triton project and help improve the systems that support performance-critical engineering work across OpenAI. In this role you will: Improve development workflows for engineers working on model performance infrastructure Design and improve CI/CD, release, validation, and testing pipelines Build and maintain tools that improve reliability, iteration speed, and engineering confidence Partner closely with engineers to identify friction in testing, debugging, deployment, and development workflows Contribute to infrastructure efforts that support performance-critical training and inference systems Help improve developer experience across Python-heavy codebases and performance-oriented infrastructure Work in a high-context, ambiguous environment where ownership and good judgment matter You might thrive in this role if: You are motivated by enabling the people around you and helping engineers do their best work You have strong experience with CI/CD, developer infrastructure, testing systems, tooling, or build/release workflows You are highly collaborative, empathetic, and comfortable partnering deeply with technical teams You are strong in Python and enjoy building reliable, scalable developer tools and infrastructure You have experience improving large-scale engineering work

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve o

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are looking for a systems-minded engineer to help advance our kernel development, performance engineering, and hardware-software co-design capabilities, with a particular focus on AI-assisted workflows and tooling. This person will work at the intersection of kernel optimization, developer tooling, observability, and research infrastructure, helping us improve both how production kernels are built and optimized, and how future hardware-software systems are designed and evaluated. The role is ideal for someone who is excited by low-level performance work, but also sees AI and automation as powerful tools for accelerating engineering velocity. You will help define the future of kernel engineering in the era of AI-assisted development. In this role, you may: Build developer tooling and workflows that make kernel development and performance optimization faster, more scalable, and easier to debug, integrate, and deploy. Develop observability, diagnostics, and validation infrastructure that makes AI-assisted optimization systems more interpretable, reliable, and effective. Optimize production kernels end to end by formulating optimization problems, running search loops, analyzing bottlenecks, debugging generated implementations, and landing improvements into production. Design abstractions, interfaces, and automation systems that accelerate kernel optimization, correctness validation, and hardware-software co-design. Improve AI-assisted optimization systems for sp

About the Team The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments as well as other OpenAI products worldwide. We operate across the full funnel - from awareness and acquisition through activation, retention, and expansion - using a combination of global performance marketing, AI-powered workflows, in-product optimization, insights, experimentation, and creative ops engineering. About the Role We are hiring a B2B Enterprise Paid Marketing leader to build and scale global demand generation across channels, including Paid Search, Paid Social, Programmatic, Affiliate, and emerging channels. This leader will define channel strategy for complex enterprise buying journeys, connect paid media to account-based marketing (ABM) programs, and own performance from target-account engagement through pipeline and revenue. You will partner closely with Sales, GTM, field marketing, product marketing, marketing operations, data science, finance, design, and product to translate enterprise priorities into coordinated campaigns. This team combines rigorous measurement with close sales alignment, rapid experimentation, and a deep understanding of buying committees, long sales cycles, and regional enterprise needs. In this role, you will: Own global paid channel strategy across Paid Search, Paid Social, Programmatic, Affiliate, and other channels for enterprise audiences, including planning, forecasting, budget pacing, audience design, and performance management. Build and scale ABM programs across priority accounts and segments, coordinating paid media with sales outreach, field marketing, executive engagement, events, and lifecycle programs. Partner with Sales and GTM leadership to align investment with account priorities, territories, pipeline goals, and product or solution plays. Optimize performance across the enterprise funnel, from account engagement and qualified demand through opportunities, pipeline, and revenue, with clear attribution

About the Team The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments as well as other OpenAI products worldwide. We operate across the full funnel - from awareness and acquisition through activation, retention, and expansion - using a combination of global performance marketing, AI-powered workflows, in-product optimization, insights, experimentation, and creative ops engineering. About the Role We are hiring a B2B Paid Marketing leader to own and scale global acquisition across channels, including Paid Search, Paid Social, Programmatic, Affiliate, and emerging channels. This leader will define channel strategy across self-serve and sales-assisted journeys, develop programs for distinct business segments, and own measurable impact from demand creation through customer growth. You will partner closely with Sales, GTM, product marketing, lifecycle, product, engineering, data science, finance, design, and marketing operations to connect paid campaigns to the right buying journeys. This team thrives on rapid iteration, rigorous measurement, and close feedback loops that improve lead and account quality, conversion, pipeline, and long-term customer value. In this role, you will: Own global paid channel strategy across Paid Search, Paid Social, Programmatic, Affiliate, and other channels for B2B audiences, including planning, forecasting, budget pacing, audience design, and performance management. Build and optimize programs across self-serve, product-led, and sales-assisted motions, adapting channel strategy to segment, use case, buyer, and funnel stage. Partner with Sales and GTM teams to align campaigns with revenue priorities, product launches, solution plays, account segments, and regional opportunities. Optimize full-funnel performance from demand capture and qualified leads through activation, pipeline, conversion, expansion, and long-term customer value. Develop B2B audience, offer, and messaging strategies for decision-mak

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role Modal's LLM inference platform delivers frontier performance for open-source models with best-in-class elasticity and developer experience, made in part possible by our custom runtime with GPU memory snapshots and multi-cloud substrate . We're looking for a leader to own the direction and execution of this platform to continue to establish us as the clear market leader, working closely with customers like Cognition, Doordash, Ramp, and many more. You'll be leading a group of highly talented engineers working on our market-leading LLM inference offering, spanning the serving stack, routing infrastructure, internal agentic optimization platform, and the user-facing product surface area. This is a hands-on leadership role — expect to split your time between technical contribution, product shaping and people management depending on what the team needs. You'll set direct

About the Team: The Database Systems team specializes in high-performance distributed databases. Our team built Rockset, the real-time search, analytics, and vector database that powers all vector search and retrieval augmented generation (RAG) at OpenAI. In addition to retrieval, as an online database, Rockset powers core functionality across all of OpenAI's product lines and many critical internal use cases. About the Role : We are looking for engineers passionate about distributed systems, close-to-the-metal performance optimization (our core engine is written in C++), and building scalable database infrastructure from the ground up. As an engineer on the Database Systems team, you'll contribute to the core database engine, driving improvements across ingestion, query execution, indexing, and storage. You'll partner with teams across OpenAI to unlock new product capabilities and help scale online database reliability and throughput as usage grows by orders of magnitude. In this role you will: Design, build, and operate high-performance distributed systems Identify and resolve performance bottlenecks to scale infrastructure to the next order of magnitude Define long-term technical direction and guide system evolution Collaborate with product, engineering, and research teams to deliver scalable and reliable infrastructure Dig deep into complex production issues across the stack Contribute to incident response, postmortems, and best practices for system reliability You might thrive in this role if you: Have significant experience building, scaling, and optimizing distributed systems at scale Are curious about database internals, storage engines, or low-latency query systems Enjoy debugging challenging performance issues in complex, high-throughput systems Have experience operating production clusters at scale (e.g., Kubernetes or other orchestration systems) Think rigorously about scalability, correctness, and reliability Thrive in fast-paced environments with high

About the Team The Business Data Science team uses data and analytics to optimize business performance, drive growth, and foster meaningful partnerships, with the goal of ensuring the sustained and impactful expansion of OpenAI's initiatives to maximize the benefits of AGI for all of humanity. We partner with Sales (GTM), Marketing, Partnerships, Support, Finance, Product, and Growth. About the Role As a member of our Business Data Science team, you will help build a data-driven culture around insight generation, decision making, and strategy at OpenAI. This role is focused on driving customer success within our business products (ChatGPT Team, ChatGPT Enterprise, and API). You will work on projects such as identifying opportunities for interventions within a customer lifecycle to drive activation & onboarding, identifying target audiences for new feature launches, and measuring the efficacy of emails, events, and other interventions to drive ongoing engagement with our products. This role is based in San Francisco, CA or New York, NY. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Embed with our Customer Success organization as a trusted partner, uncovering new ways to drive customer adoption and engagement of our business products. Establish key metrics, run experiments, and perform analysis to help us understand the incrementality of our efforts to drive adoption/engagement. Proactively surface insights and opportunities to drive engagement and growth. Build tools and systems for stakeholders to self-serve routine data and insights freeing up time to work on more leveraged analyses. Become an expert in OpenAI’s data and systems. Through partnership with Data Eng, Finance and other business teams, you will self-serve all the underlying data for our business and derive insights from them. Partner with other data scientists across the company to share knowledge and continually