Member of Technical Staff - Robot Learning

Stealth Robotics; Boston (MA); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Early-stage equity

Publication opportunities

Research collaboration

Job summary

Stealth Robotics is seeking a Member of Technical Staff focused on Learning & Control to advance learned behaviors for physical tasks. You will take ideas from training through hardware experiments, focusing on generalization, adaptation, and recovery when conditions change.

You’ll collaborate with perception, controls, and integration engineers to deploy policies on real robots, design rigorous experiments, and publish when appropriate. Equity is offered for early-stage contributions.

Qualifications

• 2+ years of relevant research or engineering experience, including graduate research.
• MS or PhD preferred; equivalent experience welcome.

Responsibilities

• Develop and evaluate learning-based policies for robotic tasks.
• Improve performance across objects, environments, and task variations.
• Develop recovery behaviors for failed actions and unexpected conditions.
• Design evaluations to measure task success, robustness, and data efficiency.
• Use demonstrations and experimental results to guide data collection and training.
• Collaborate with perception, controls, and integration engineers to deploy policies on hardware.

Skills

Python

Deep learning framework

Learned policies

Real-robot evaluation

Imitation learning

Reinforcement learning

Vision-Language-Action models

Experimental design

Trade-off analysis

Education

MS or PhD in robotics, CS, or related field

Job description

Member of Technical Staff — Learning & Control

We’re an early-stage robotics company operating in stealth, building intelligent systems that can learn and perform useful work in the physical world. Our team works across hardware, software, and machine learning, with close collaboration between research and engineering.

We’re looking for someone to develop learned behaviors for physical tasks. You’ll take ideas from training through hardware experiments, working on generalization, adaptation, and recovery when conditions change.

What you’ll do

• Develop and evaluate learning-based policies for robotic tasks.
• Improve performance across objects, environments, and task variations.
• Develop recovery behaviors for failed actions and unexpected conditions.
• Design evaluations that measure task success, robustness, and data efficiency.
• Use demonstrations and experimental results to guide data collection and training.
• Work with perception, controls, and integration engineers to deploy policies and understand failures on hardware.

What we’re looking for

• Strong Python skills and experience with a modern deep learning framework.
• Practical experience developing learned policies and evaluating them on real robots.
• Familiarity with imitation learning, reinforcement learning, or vision-language-action models.
• The ability to design rigorous experiments, interpret results carefully, and explain technical trade-offs.
• Typically 2+ years of relevant research or engineering experience, including graduate research.
• An MS or PhD in robotics, computer science, or a related field is preferred. Equivalent experience and strong recent PhD graduates are welcome.

Working with us

You’ll work closely with experienced researchers and engineers, have meaningful ownership of technical decisions, and receive substantial early-stage equity. There are opportunities to publish research, with publication plans aligned with company IP and disclosure policies.

We’ll share more about the product and technical direction during the interview process. Please include a project, paper, or demonstration and describe your contribution.