Robotics Research Engineer / Scientist — Robot Learning

Maxinsights
Santa Clara, US
On-site

Who this role is best for

Candidates with advanced robotics or ML expertise and hands-on deployment experience will find this research-focused role in Santa Clara ideal for pushing real-world robotic capabilities.

Best fit for

  • Individuals with a PhD or equivalent hands-on experience in robotics or machine learning who enjoy practical experimentation.
    — “PhD in Robotics, Computer Science, Machine Learning, or a related field; alternatively, substantial hands-on experience developing learning-based robotic systems.”
  • Those with a background in visuomotor learning and a history of designing rigorous evaluation experiments.
    — “Design rigorous experiments to evaluate task success, robustness, and generalization.”

Things to consider

  • ROS integration or traditional control systems experience alone may not be sufficient for this role.
    — “ROS integration or traditional control systems experience alone is not sufficient for this role.”
  • The role emphasizes real-world deployment, not just theoretical research.
    — “bridge the gap between research and practical robotic capabilities”

How to stand out

  • Highlight real-world deployment of learning-based robotic policies in your resume and interview examples.
    — “Deploy trained policies on physical robots and debug performance in real-world environments.”
  • Emphasize experience with imitation learning, reinforcement learning, or VLA models in your application materials.
    — “Apply methods such as imitation learning, reinforcement learning, diffusion policies, and VLA models.”
  • Demonstrate your skills in creating data collection and training pipelines for robotic systems.
    — “Design data collection and training pipelines using robot demonstrations and other relevant datasets.”
  • Include specific examples of improving task success rates or generalization through model or data refinement.
    — “Experience improving task success rates, robustness, or generalization through model or data improvements.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • developed and trained learning-based policies for robotic manipulation
  • deployed trained policies on physical robots
Typical background
roboticsmachine learningrobot learning

Skills & requirements

Required

RoboticsMachine LearningRobot LearningPolicy TrainingData CollectionModel Evaluation

Preferred

Bimanual ManipulationDexterous ManipulationGeneralization Across Tasks

Stack & domain

PythonPyTorchRoboticsMachine Learning

About the role

Original posting from Maxinsights via Ashby

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

Source: Maxinsights careers (Ashby)

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