Robotics Machine Learning Engineer

This company is building and commercializing rugged, multi-purpose humanoid robots designed to perform real, economically meaningful work in unstructured environments. The founding team has deep roots in humanoid robotics, bionics, and large-scale hardware product development, with experience delivering robust systems that have operated in extreme and highly demanding real-world conditions.

The mission is focused on shipping reliable, well-designed robotic products at scale, with a strong emphasis on practical deployment, customer impact, and long-term robustness rather than research-only prototypes.

Role Summary: Machine Learning Engineer (Robotics)

This is an early, foundational machine learning role within a humanoid robotics company. The engineer will help define and execute the overall machine learning strategy, shaping both the architecture and practical application of ML across the humanoid robot stack.

The role spans multiple embodied AI domains, including manipulation, navigation, locomotion, and perception, with a strong emphasis on deploying models that work reliably on real hardware. The team is particularly interested in engineers who have taken ML systems from concept through to production, though strong research-driven candidates with high execution ability are also considered.

Core Responsibilities

• Contribute to the design and development of the machine learning software stack and its application across manipulation, navigation, locomotion, and perception.

• Partner with the ML team to define and execute a roadmap for model development, informed by state-of-the-art research and real-world constraints.

• Build, test, and deploy machine learning pipelines, supporting infrastructure, and data collection systems.

• Monitor, evaluate, and iterate on model performance in real-world robotic deployments.

• Collaborate with external research partners, including academic institutions and industry teams.

• Play a key role in growing and shaping the machine learning and autonomy teams over time.

Ideal Background

• Several years of experience applying machine learning to robotics or embodied AI systems.

• Strong hands-on experience with modern deep learning frameworks.

• Experience using cloud infrastructure to train models, manage datasets, and support ML pipelines.

• Solid understanding of contemporary robot learning approaches such as behavior cloning, reinforcement learning, and world models.

• Practical awareness of the challenges involved in deploying neural networks on physical robotic systems.

• Strong software engineering fundamentals and a bias toward building maintainable, production-ready systems.

• Comfort operating in fast-moving, ambiguous startup environments with high ownership.

Bonus Signals

• Advanced degree in robotics, computer science, machine learning, or a related field.

• Publications at top-tier ML or robotics conferences.

• Direct experience deploying robots, collecting large-scale datasets, and training models that perform reliably in production.