Machine Learning Engineer, Robotics and Physical Ai - Burlington, Ma

Motion Recruitment Partners LLC; Burlington (MA); Hybrid; USD 135,000 - 175,000; Full time

Benefits offered by this job

Medical Insurance

Dental Benefits

Vision Benefits

Flexible PTO

401(k) with employer match

Parental Leave

Professional Development Assistance

Team events

Job summary

Motion Recruitment Partners LLC in Burlington, MA, seeks a Machine Learning Engineer, Robotics & Physical AI to design, train, deploy, and optimize AI for robotic systems. You will collaborate with software, robotics, controls, and hardware engineers to bring AI models into production on real-world hardware.

The role emphasizes ownership of end-to-end development, from experiments and architecture through deployment, validation, and ongoing improvement of AI systems on physical machines in a

Qualifications

• 4+ years of experience in ML, AI, robotics or related field.
• Strong Python programming.
• Experience deploying ML models to production.

Responsibilities

• Design, train, evaluate, and deploy ML models for robotic/autonomous systems.
• Develop perception and decision-making for intelligent machines.
• Integrate AI models with production hardware and software stacks.
• Build data collection, training, evaluation, and monitoring pipelines.
• Optimize models for edge devices and real-time robotics.
• Prototype and validate new ML techniques to improve autonomy.

Skills

4+ years ML/AI/Robotics

Python

Production deployment

PyTorch or TensorFlow

Computer vision / perception

Linux

Data pipelines / evaluation

Hardware interaction

Git / CI/CD

Education

Bachelor's in CS / ML / Robotics / EE

Tools

ROS / ROS 2

ONNX Runtime / TensorRT

Edge AI platforms

Job description

An innovative robotics and Physical AI startup based in Burlington, Massachusetts is seeking a Machine Learning Engineer, Robotics & Physical AI to help build intelligent systems that bridge advanced machine learning with real-world automation and robotics.

This role is ideal for an engineer who enjoys moving beyond model development and into deployment, working directly with robotic platforms, sensors, vision systems, and automation equipment. You will collaborate closely with software, robotics, controls, and hardware engineers to design, train, deploy, and optimize machine learning solutions that drive autonomous decision-making in real-world environments.

As a key member of a growing engineering team, you will have significant ownership over the development lifecycle, from experimentation and model architecture through deployment, validation, and continuous improvement of AI systems operating on physical hardware.

This is a hybrid position located in Burlington, MA, with an expectation of approximately 2-3 days onsite per week.

Required Skills & Experience

• 4+ years of experience in Machine Learning, Artificial Intelligence, Robotics, or a related field
• Strong programming experience in Python
• Experience developing and deploying machine learning models in production environments
• Solid understanding of deep learning frameworks such as PyTorch or TensorFlow
• Experience working with computer vision, perception systems, sensor fusion, or robotics-related ML applications
• Familiarity with Linux development environments
• Strong understanding of data pipelines, model evaluation, experimentation, and performance optimization
• Experience implementing ML solutions that interact with real-world systems or hardware
• Knowledge of software engineering best practices including Git, testing, and CI/CD
• Bachelor's degree in Computer Science, Machine Learning, Robotics, Electrical Engineering, or a related technical discipline

Desired Skills & Experience

• Experience with robotics frameworks such as ROS or ROS 2
• Hands-on experience with autonomous systems, industrial robotics, drones, or intelligent automation
• Experience deploying models using ONNX Runtime, TensorRT, or edge AI platforms
• Knowledge of reinforcement learning, imitation learning, or motion-planning systems
• Computer vision experience utilizing OpenCV and modern vision architectures
• Experience with simulation environments such as Isaac Sim, Gazebo, or digital twin platforms
• Familiarity with embedded systems and real-time applications
• Startup experience where engineers take ownership of architecture and product decisions

What You Will Be Doing

Daily Responsibilities

• Design, train, evaluate, and deploy machine learning models that drive robotic and autonomous systems
• Develop perception, vision, and decision-making capabilities for next-generation intelligent machines
• Work alongside robotics and software engineers to integrate AI models into production hardware environments
• Build data collection, training, evaluation, and monitoring pipelines
• Optimize models for deployment on edge devices and real-time robotic platforms
• Develop and improve computer vision systems using cameras, sensors, and multimodal data sources
• Prototype and validate new machine learning techniques that improve system performance and autonomy
• Create testing and validation frameworks to ensure reliable model behavior in production environments
• Analyze system performance and continuously refine models using operational and experimental data
• Contribute to the technical direction of emerging Physical AI and robotics products

The Offer

• Hybrid role based in Burlington, MA
• Competitive base salary: $135,000 - $175,000
• Annual performance bonus
• Meaningful startup equity package
• Opportunity to work on cutting-edge robotics, machine learning, and Physical AI technologies
• High-impact role with visibility across leadership and engineering teams
• Fast-paced startup environment with significant ownership and technical influence

Benefits

• Medical Insurance
• Dental Benefits
• Vision Benefits
• Flexible Paid Time Off (PTO)
• 401(k) with company contribution
• Paid Parental Leave
• Professional Development Assistance
• Team Events and Company Gatherings

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.