Why This Job is Featured on The SaaS Jobs
Training performance work sits at the infrastructure layer that increasingly differentiates modern SaaS, especially for AI-enabled products where model iteration and serving economics shape what can be shipped. This role is notable because it focuses on the runtime that underpins distributed training, an area where reliability and throughput translate directly into product capability and cost control. The remit spans the full path from research experimentation to large-scale runs, which is a common pressure point as SaaS companies operationalise advanced ML.
From a SaaS career perspective, the experience compounds in several directions: rigorous performance engineering, production-grade observability, and the systems thinking needed to make platform investments that pay off across multiple internal customers. Work that touches scheduling, checkpointing, and data movement also builds intuition for multi-tenant infrastructure and operational trade-offs, skills that transfer across ML platforms, data platforms, and core cloud infrastructure teams.
The role fits engineers who prefer measurement-driven iteration, enjoy tracing bottlenecks across layers, and are comfortable partnering with both researchers and platform stakeholders. It is well suited to someone who wants their impact to be visible in utilisation, uptime, and developer productivity rather than in end-user features, and who values deep technical ownership within a SaaS-adjacent ML stack.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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:
Profile end-to-end training runs to identify performance bottlenecks across compute, communication, and storage.
Optimize GPU utilization and throughput for large-scale distributed model training.
Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.
Implement model graph transforms to improve end to end throughput.
Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.
Partner with researchers to ensure new model architectures scale efficiently during pre-training.
Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.
You might thrive in this role if you:
Love optimizing performance and digging into systems to understand how every layer interacts.
Have strong programming skills in Python and C++ (Rust or CUDA a plus).
Have experience running distributed training jobs on multi-GPU systems or HPC clusters.
Enjoy debugging complex distributed systems and measuring efficiency rigorously.
Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.
Are comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.
Nice to have:
Familiarity with NCCL, MPI, or UCX communication libraries.
Experience with large-scale data loading and checkpointing systems.
Prior work on training runtime, distributed scheduling, or ML compiler optimization.
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 our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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