Robotics ML Engineer: Lead for Production Deployments

Strive Robotics; Somerville (MA); On-site; USD 150,000 - 230,000; Full time

Job summary

Strive Robotics is seeking a lead robot learning engineer to build and deploy learned capabilities for robots operating in manufacturing environments. You will own training architecture and implementation, working hands-on across models, data, and evaluation to deliver reliable systems for customer pilots.

You will apply reinforcement learning, fine-tune vision-language-action models, and integrate specialized models with other approaches, using sensor data, force feedback, and robot state to

Qualifications

• Strong machine learning fundamentals and ability to train or adapt foundation models, build production ML infrastructure, and evaluate performance against real operating requirements.
• Experience deploying learned systems on physical robots, diagnosing failures and improving reliability under conditions that differ from training.
• The technical depth and independence to choose methods based on the problem, make architecture decisions, and debug across data, models, sensors, and robot behavior.

Responsibilities

• Train, adapt, and deploy models for perception, manipulation, locomotion, and machine operation, including CNC workflows at customer pilot sites.
• Apply reinforcement learning, fine-tune vision-language-action models, and combine specialized models with other approaches suited to each task.
• Build data collection, training, and evaluation pipelines that connect field performance to model improvements.

Skills

ML fundamentals

Foundation models

Production ML infra

Deploy learned systems

Robot systems reliability

Job description

Strive Robotics is seeking a lead robot learning engineer to build and deploy learned capabilities for robots operating in manufacturing environments. You will own training architecture and implementation, working hands-on across models, data, and evaluation to deliver reliable systems for customer pilots.

You will apply reinforcement learning, fine-tune vision-language-action models, and integrate specialized models with other approaches, using sensor data, force feedback, and robot state to