Staff Machine Learning Engineer, Agentic Systems
- Design and implement System 2 architectures for humanoid robot control, including planning, reasoning, memory, and tool-use frameworks.
- Develop logging, observability and evaluation methodologies to guide performance improvement and measure reasoning quality, safety, reliability, and generalization.
- Collaborate cross-functionally with behavior, controls, perception, and product teams to ship end-to-end capabilities.
- Engage with customers and internal stakeholders to understand real-world use cases and ensure solutions are practical, reliable, and impactful.
- Stay at the forefront of agentic research and translate state-of-the-art techniques into production systems.
- Contribute to a strong engineering culture through code reviews, testing, documentation, and thoughtful system design.
- 5+ years of professional software engineering experience, including significant work on LLM-driven or agentic systems.
- Hands-on experience building or deploying agentic architectures (e.g., coding agents, tool-using LLM systems, autonomous task agents).
- A track record of improving agentic system performance through evaluation and benchmarking.
- Strong fundamentals in data structures, algorithms, distributed systems, and software architecture.
- Demonstrated ability to ship reliable, maintainable, and well-tested software.
- Ability to reason about safety, failure modes, and robustness in autonomous systems.
- Experience working with robotics systems, vision models, and VLAs.
- Strong proficiency in Typescript, Python and/or C++, with comfort operating across large codebases.
- Knowledge and application of coding agents.
- History of pushing the state of the art through research, open-source contributions, or novel production systems.
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