OpenAI · San Francisco
Software EngineeringResearchRemoteFullTimeposted 3mo ago
The Post-Training Frontiers team is responsible for training the frontier agents OpenAI ships to the world (GPT-Next). We train the flagship agentic models behind Codex, ChatGPT, and the API through large-scale reinforcement learning.
The team’s work spans four areas. First, execution and science: working with teams across OpenAI to decide what can go into the final model and how, using scientific experiments and evals that are representative of the final pipeline so issues can be recognized early. Second, RL scaling: executing the final large-scale reinforcement learning run, making sure GPUs are used efficiently and training stays healthy. Third, research: improving horizontal capabilities like instruction following, factuality, memory, and multi-agent behavior, where the team’s broad visibility helps identify cross-cutting improvements across teams and domains. Fourth, engineering: maintaining the infrastructure stack and internal tools to ensure that both the final run and all integrations go as smoothly as possible and that the systems are easy to work with.
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.
Keep large-scale async 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.
Improve the reliability and efficiency of RL training runs.
Help researchers who are developing infrastructure-heavy integrations, such as multi-agent capabilities or memory.
Turn recurring operational issues into better tools, systems, processes, automations, 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 hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.
Are driven by having a large impact on the world and want to train and ship the best model in the world to our users.
Are a strong generalist engineer with experience in some layer of ML infrastructure.
Learn extremely quickly and are comfortable operating across unfamiliar layers.
Are highly independent and can plan and fix issues on your own when needed.
Have worked on RL, inference, scaling, training systems, orchestration, or adjacent infrastructure.
Are a strong debugger with high ownership, low ego, and excellent communication skills.
Can land in a messy area with tight timelines, become useful quickly, and gradually raise the quality of the whole system.
Thrive in fast-moving environments where reliability, speed, and judgment matter.
Are comfortable collaborating with multiple teams across OpenAI, both in research and in product.
Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
Are very proactive about fixing any important issue that you encounter.
Are very good at prioritizing your work and often take an 80/20 approach, moving on to different problems after optimizing for low-hanging fruit.
Enjoy helping other people with their work.
Continuously try to automate the annoying parts of your work.
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.
A background in performance optimization, scaling, or production-critical infrastructure.
Experience directly supporting researchers or fast-moving model teams.
About OpenAI
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