Founding Research Engineer

We’re hiring a Founding Research Engineer to help build and eventually lead a team of exceptional engineers and researchers. Your most important responsibility will be accelerating the research loop: ideating directions, leading a team to build them into working systems, and deploying those systems to gather feedback. The speed with which you iterate is central to our success. In your first 30 days, you’ll plan a new capability for our API, launch training runs and implement your plan, then deploy it on robots in factories. This role combines collaboration with hand-on work. You’ll work with the founders to set technical direction, take ownership of execution, and contribute directly to the systems you lead. You should be comfortable designing complex systems and diving into the implementation yourself. What You’ll Do

• Grow and lead a small, high-performing team, setting clear expectations and maintaining a high bar for technical quality and execution.
• Set technical direction and make foundational architecture and system-design decisions, with a focus on reliability and performance.
• Build core systems, including agent harnesses, robot simulation and evaluation environments, trace-collection pipelines, and infrastructure for post-training models.
• Recruit and close exceptional engineering and research talent.

Skills and Qualifications

• Strong research judgment: you can turn open-ended ideas into concrete experiments, interpret the results, and iterate quickly.
• Strong foundations in machine learning, computer science, mathematics, or a related technical discipline, developed through research, industry, or independent work.
• High agency and comfort operating quickly in an ambiguous, early-stage environment.

Preferred qualifications We don’t expect candidates to meet every preferred qualification, but you should have depth in some of the following:

• Experience building LLM agents that use tools, reason over long horizons, or operate in interactive environments.
• Experience with post-training methods such as supervised fine-tuning or reinforcement learning, including running and debugging distributed training jobs.
• Experience building evaluation systems, synthetic-data pipelines, reward functions, or infrastructure for collecting and analyzing model traces.
• Experience deploying ML systems in the real world, especially on robots or other systems with latency, reliability, and hardware constraints.
• Experience leading technically ambitious projects, mentoring engineers, or helping recruit and build a high-performing team.