Robotics Research Engineer / Scientist — Robot Learning

Maxinsights; Santa Clara (CA); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Vision care

Dental coverage

401(k)

Paid holidays

PTO (Paid Time Off)

Sick leave

Job summary

Maxinsights in Santa Clara, CA is seeking a Robotics Research Engineer/Scientist to develop learning-based methods enabling robots to perform complex tasks in the real world, with emphasis on imitation learning, reinforcement learning, and Vision-Language-Action models.

You will train and deploy visuomotor policies on physical robots, design data collection pipelines from demonstrations, and run rigorous experiments to measure task success, robustness, and generalization.

Qualifications

• PhD (or substantial hands-on) in robotics, CS, ML, or related field.
• Strong programming in Python and DL frameworks (PyTorch).
• Experience with learning-based robotics approaches (RL/IL/VLA).
• Understanding of robotic manipulation, perception, control, or visuomotor learning.

Responsibilities

• Develop and train learning-based policies for robotic manipulation and embodied tasks.
• Apply imitation learning, reinforcement learning, diffusion policies, and VLA models.
• Design data collection and training pipelines using robot demonstrations.
• Deploy trained policies on physical robots and debug real-world performance.
• Design rigorous experiments to evaluate success, robustness, and generalization.
• Collaborate with infra, simulation, and data teams to accelerate research.

Skills

PhD or equivalent

Python

PyTorch

Reinforcement learning

Imitation learning

Robotics perception

Education

PhD in Robotics/CS/ML or related

Tools

ROS

Git

Gazebo

Job description

Job Description:

Job Overview

We are looking for a Robotics Research Engineer / Scientist to develop learning-based methods that enable robots to perform complex tasks in the real world.

You will work on robot learning, policy training, and real-world deployment, exploring approaches such as imitation learning, reinforcement learning, and Vision-Language-Action (VLA) models. You will help bridge the gap between research and practical robotic capabilities by developing, training, and evaluating policies on real robotic systems.

This role is ideal for someone with strong robotics or machine learning expertise who enjoys hands‑on experimentation and pushing the capabilities of learning‑based robotic systems.

What You Will Own

• Develop and train learning‑based policies for robotic manipulation and other embodied tasks.

• Apply methods such as imitation learning, reinforcement learning, diffusion policies, and VLA models.

• Work on visuomotor policies, bimanual manipulation, dexterous manipulation, and generalization across tasks or environments.

• Design data collection and training pipelines using robot demonstrations and other relevant datasets.

• Deploy trained policies on physical robots and debug performance in real-world environments.

• Design rigorous experiments to evaluate task success, robustness, and generalization.

• Analyze failures and iterate on data, model architectures, and training strategies to improve performance.

• Collaborate with infrastructure, simulation, and data teams to accelerate robot learning research.

Required Qualifications

• A PhD in Robotics, Computer Science, Machine Learning, or a related field; alternatively, substantial hands‑on experience developing learning‑based robotic systems. Strong MS candidates are welcome.

• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch.

• Experience with at least one learning‑based robotics approach, such as imitation learning, reinforcement learning, diffusion policies, or VLA models.

• Understanding of robotic manipulation, perception, control, or visuomotor learning.

• Experience designing and conducting experiments to evaluate learned policies.

• Strong problem‑solving skills and the ability to translate research ideas into working systems.

Preferred Qualifications

• Experience deploying learned policies on real robots.

• Experience with bimanual or dexterous manipulation.

• Experience collecting and processing robot demonstration data.

• Experience improving task success rates, robustness, or generalization through model or data improvements.

• Research publications or contributions to open‑source robotics and ML projects are a plus.

Note: We prioritize candidates with hands‑on experience in learning‑based robotic systems. ROS integration or traditional control systems experience alone is not sufficient for this role.

Default Benefits:

• Health insurance

• Vision care

• Dental coverage

• 401(k)

• Paid holidays

• PTO (Paid Time Off)

• Sick leave