Member of Technical Staff, Deep-Tech AI; Stealth

Member of Technical Staff, Deep-Tech AI (Stealth)

• Location:
  
  Remote and/or Boston/Cambridge area
• Compensation: $200,000 to $280,000+ base, plus equity. Comprehensive benefits: health, dental, vision, and 401(k) with company match.
• Experience:
  
  Robotics, and AI/MLE experience is required.

A core, hands‑on contributor to the design, implementation and deployment of the company’s new learning architectures. Working on open‑ended problems at the intersection of robotics, machine learning and information theory, with direct ownership of technical outcomes and close collaboration with the founding team. Scope spans research and development, systems architecture, robotics integration, performance optimisation and general founding‑level ownership (technical proposals, patent contributions, customer evaluations).

Robotics experience is required. This will suit someone from a top research lab or robotics group who wants to do foundational work at the frontier, with the ownership and pace of a founding team rather than the constraints of a large organisation.

• A funded, revenue‑generating, stealth‑stage deep‑tech AI company spun out of a world‑leading research lab. The team comes from a reinforcement learning and robotics research background, with people who have built autonomous vehicles, personal robots, spacecraft, multimodal models and safety‑critical systems.
• The company is building a genuinely new approach to learning systems: minimal, online, continual, able to interact with the physical world, rather than scaling ever‑larger offline models. The work draws on algorithmic information theory, sparse reconstruction, approximation theory, harmonic analysis, and compressive sensing. It is radically more compute‑efficient than typical AI, with real paying customers and healthy economics. This is a rigorous, research‑led company, not a hype‑driven one.

Robotics experience is required. Incoming customers are robotics companies, so hands‑on robotics background is the binding filter, not a nice‑to‑have. Candidates should come from one or more of:

• A leading AI research lab with real reinforcement learning depth
• A robotics or embodied‑AI company (manipulation, autonomy, real‑world systems)
• Autonomous vehicles or safety‑critical physical systems
• Spacecraft, personal robots or other real‑time closed‑loop systems

• Hands‑on robotics experience (deploying learning systems on physical hardware in unstructured environments)
• Strong programming in Python and/or C
• Deep understanding of one or more: machine learning, reinforcement learning, control theory, compressive sensing, computer vision
• Experience with JAX, PyTorch or Tensor Flow
• Ability to take ambiguous, open‑ended problems and drive them to completion independently

• Familiarity with algorithmic information theory, sparse reconstruction, harmonic analysis or compressive sensing
• Low‑level / real‑time systems work for closed‑loop control

• 2 to 5+ years industry or research experience in robotics, ML, compressive sensing, signal processing or related
• Master's or Ph.D. in CS, EE, Robotics, Applied Mathematics or related (or equivalent experience), strong institution