Software Engineer, RL Training Infra

About the Team The Post-Training Frontiers team creates the frontier agents OpenAI ships to the world. We do the reinforcement learning training for the agentic models we ship in Codex, ChatGPT, and the API (from o1 to 5.5). Our role consists of (1) shepherding all integrations that should go into the final RL run and deciding what can make it in, (2) babysitting and scaling the final run, and (3) building the research and infra for horizontal integrations, such as improving function calling, factuality, multi-agent capabilities, memory, calibrated thinking, etc. About the Role This role focuses on keeping our frontier RL training runs fast, reliable, and unblocked. You will work across engineering and infrastructure problems as they emerge, from scaling and orchestration issues to inference bottlenecks, numerical problems, and hardware failures, as well as supporting large horizontal integrations in the big run, like multi-agent capabilities or memory. This is a role for a strong generalist who quickly learns anything needed for the task, has high attention to detail, debugs deeply, and is motivated by fixing the highest-impact problem in front of the team. In this role, you will: - Keep large-scale RL training runs moving by jumping into the most urgent engineering and infrastructure problems. - Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure. - Solve hard technical problems at the boundary between research and engineering: scaling experiments, improving training reliability, debugging distributed systems, reducing latency and cost, and making new capabilities robust under real workloads. - Improve reliability and efficiency for RL training runs. - Help researchers who are developing infra-heavy integrations, such as multi-agent capabilities or memory. - Turn recurring operational issues into better tools, systems, processes, or abstractions. - Work closely with research, infrastructure, and partner teams during tight model run timelines. - Become useful quickly in messy, ambiguous areas where ownership matters more than a perfectly scoped project. - Debug failures that cut across model behavior, training data, RL systems, evaluation infrastructure, serving systems, and agent harnesses, then turn those failures into hypotheses, fixes, and durable improvements. You might thrive in this role if you: - Want to train and ship our frontier models and ensure we make agents genuinely useful for developers, enterprises, researchers, and everyday users. - Are a strong generalist engineer with experience in some layer of ML infrastructure. - Have worked on RL, inference, scaling, training systems, orchestration, or adjacent ML infrastructure. - Learn extremely quickly and are comfortable operating across unfamiliar layers. - Are a strong debugger with high ownership, low ego, and excellent communication. - Can land in a messy area with tight timelines, become useful quickly, and gradually raise the quality of the whole system. - Are energized by fast-moving environments where reliability, speed, and judgment matter. - Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous. Nice to have: - Experience supporting large-scale model training, async RL systems, or high-throughput ML infrastructure. - Experience debugging distributed systems across GPUs, networking, orchestration, or inference stacks. - Background in performance optimization, scaling, or production-critical infrastructure. - Experience working directly with researchers or fast-moving model teams. 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 his

You might thrive in this role if you: - want to train and ship our frontier models and ensure we make agents genuinely useful for developers, enterprises, researchers, and everyday users. - are a strong generalist engineer with experience in some layer of ML infrastructure. - have worked on RL, inference, scaling, training systems, orchestration, or adjacent ML infrastructure. - learn extremely quickly and are comfortable operating across unfamiliar layers. - are a strong debugger with high ownership, low ego, and excellent communication. - can land in a messy area with tight timelines, become useful quickly, and gradually raise the quality of the whole system. - are energized by fast-moving environments where reliability, speed, and judgment matter. - like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous. Nice to have: - experience supporting large-scale model training, async RL systems, or high-throughput ML infrastructure. - experience debugging distributed systems across GPUs, networking, orchestration, or inference stacks. - background in performance optimization, scaling, or production-critical infrastructure. - experience working directly with researchers or fast-moving model teams.