TL, Research Inference

San Francisco, United StatesOn-site

Responsibilities

- Own and optimize core execution paths, including model execution, memory management, batching, and scheduling.
- Implement and optimize inference-critical operators and kernels informed by real-world workloads.

ABOUT THE ROLE In this role, you will build the systems that enable advanced AI models to run efficiently at scale.
By developing and evolving high-performance inference infrastructure, you will enable researchers to explore new ideas with a clear understanding of their computational and systems implications.
Instead, it is a research-enabling systems role focused on performance, correctness, and realism - ensuring that AI research is grounded in what can actually scale. IN THIS ROLE,
YOU WILL: - Design and build high-performance inference runtimes for large-scale AI models, with a focus on efficiency, reliability, and scalability.
- Develop and improve distributed inference across multiple GPUs, including parallelism strategies, communication patterns, and runtime coordination.
- Contribute to observability, correctness, and reliability of large-scale AI systems.
- Are comfortable with GPU-centric performance engineering, including memory behavior and latency/throughput tradeoffs.
- Have worked on multi-GPU or distributed systems involving batching, scheduling, or runtime coordination.
About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence

ABOUT THE TEAM The Foundations team focuses on how model behavior changes as we scale models, data, and compute.
The team studies the interactions between model architecture, optimization, and training data, and uses those insights to guide how new models are designed and trained.
You will operate at the intersection of model research and systems engineering, translating new architectural ideas into high-performance inference systems that surface real tradeoffs in performance, memory, and scalability.
Your work will directly influence how models are designed, evaluated, and iterated on across the research organization.
- Partner closely with research teams to ensure new model architectures are supported accurately and efficiently in inference systems.
- Diagnose and resolve performance bottlenecks through profiling, benchmarking, and low-level debugging.
- Can reason end-to-end about inference pipelines, from request handling through execution and output streaming.
- Are able to understand research ideas and implement them within real system and performance constraints.
- Enjoy solving hard, ambiguous systems problems that only emerge at scale.
- Prefer hands-on technical ownership and execution over abstract design work.