> Markdown version of [/jobs/ext/4629642-staff-machine-learning-engineer-agentic-systems](https://www.wearedevelopers.com/jobs/ext/4629642-staff-machine-learning-engineer-agentic-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer, Agentic Systems - **Company:** Boston Dynamics - **Location:** Waltham, MA, United States - **Salary:** $155,000.0 - $235,000.0 - **Contract:** Permanent contract - **Skills:** Code Review, Data Structures, Distributed Systems, Machine Learning, Software Engineering, Data Logging, Large Language Models, AI Coding Agents, Agentic-AI - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/7_84F490F7832C24B2EFB0461C5592F275 ## About the Role 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.Requirements5+ 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.PreferredExperience ## Description ResponsibilitiesDesign 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.