Machine Learning Engineer, Distributed Data Systems - Robotics

The OpenAI Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples' lives.

As a Research Engineer, Distributed Data Systems, you will design and scale the infrastructure that powers large-scale multimodal training and evaluation at OpenAI. You'll manage distributed data pipelines, collaborate closely with researchers to translate requirements into robust systems, and harden pipelines that serve as the backbone for OpenAI's rapid iteration cycles.

We're looking for engineers who are detail-oriented, have strong experience with distributed systems, and excel at building reliable infrastructure in high-stakes environments.

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.

- Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, machine learning infrastructure while ensuring scalability, reliability, and security.

- Ensure our data platform can scale by orders of magnitude while remaining reliable and efficient.

- Partner with researchers to deeply understand requirements and translate them into production-ready systems.

- Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation.

- Have strong experience with distributed systems and large-scale infrastructure with a strong interest in data.

- Are detail-oriented and bring rigor to building and maintaining reliable systems.

- Demonstrate excellent software engineering fundamentals and organizational skills.