Robotics Engineer: Foundation Model Training

Generalrobotics

Redmond +1 more

Who this role is best for

Strong fit for researchers with deep expertise in robotics and foundation models who can collaborate across teams and geographies.

Best fit for

Candidates with strong research backgrounds in simulation and reinforcement learning
— “Scale existing simulation platforms for data generation and reinforcement learning across heterogeneous scenes and tasks.”
Individuals with experience in building machine learning models for robotic perception and action
— “Develop and scale foundation models for robotic perception and action.”
Professionals who can transition research into production-quality work
— “Implement, evaluate, and clearly communicate research as reusable, production-quality work.”

Things to consider

Work authorization requirements may pose a barrier for non-local candidates
— “Must obtain and maintain work authorization in the country of employment.”
The role involves significant cross-office collaboration and travel
— “Our Singapore team works closely with the US team on GRID's research and development as an extended research facility, and to support our customers in the region.”

How to stand out

Highlight experience with sim-to-real transfer in robotics applications
— “Improve sim-to-real transfer and reliability under real-world deployment constraints.”
Emphasize contributions to model generalization across tasks and environments
— “Advance generalization beyond demonstrated tasks and robust spatial understanding.”
Showcase expertise in large-scale pretraining and data-mixture design
— “Experience with large-scale pretraining, data-mixture design, post-training, or reinforcement learning.”
Demonstrate familiarity with NVIDIA Isaac Sim or MuJoCo for simulation scaling
— “Experience scaling simulation platforms such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or ManiSkill for learning and data generation.”
Include track records of translating research into validated implementations
— “A track record of taking research from hypothesis through implementation and robust validation.”

Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Team

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

Skills & requirements

Required

Python

Stack & domain

PythonPyTorchNvidia Isaac SimMujocoManiskillRoboticsMachine LearningComputer VisionReinforcement LearningSimulation

About the role

Original posting from Generalrobotics via Ashby

About this Role
We're hiring Research Engineers to develop foundation models for GRID. You'll work across model architecture, large-scale training, reinforcement learning, simulation, and evaluation to build robotic behaviors that generalize across tasks, environments, and embodiments.
We value exceptional depth in one or more relevant areas; candidates are not expected to have experience across all of them.
Responsibilities
Formulate research hypotheses and design rigorous experiments informed by deployment needs.
Develop and scale foundation models for robotic perception and action.
Leverage simulation and diverse datasets for pretraining, post-training, reinforcement learning, and evaluation.
Scale existing simulation platforms for data generation and reinforcement learning across heterogeneous scenes and tasks.
Advance generalization beyond demonstrated tasks and robust spatial understanding.
Improve sim-to-real transfer and reliability under real-world deployment constraints.
Develop Auto-Engineering methods for scalable and robust deployment.
Implement, evaluate, and clearly communicate research as reusable, production-quality work.
Minimum Qualifications
Bachelor's degree in Computer Science, Robotics, a related technical field, or equivalent practical experience.
Research or applied experience in machine learning, robotics, or computer vision.
Strong Python programming skills and experience with PyTorch.
Experience developing and evaluating machine-learning models or robotics algorithms.
Experience with at least one robotics simulation platform.
Familiarity with inverse kinematics, dynamics, and robotic manipulation.
Demonstrated research ability through publications, substantial research contributions, or equivalent industry work.
Must obtain and maintain work authorization in the country of employment.
Desired Qualifications
MS or PhD in Machine Learning, Robotics, Computer Science, or a related field, or equivalent industry research experience.
First-author publications at NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, or ICRA.
Hands-on experience with Vision-Language-Action models, world models, world action models, diffusion or flow-matching policies, transformers, reinforcement learning, or imitation learning.
Experience with large-scale pretraining, data-mixture design, post-training, or reinforcement learning.
Experience scaling simulation platforms such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or ManiSkill for learning and data generation.
Experience with sim-to-real transfer and evaluation on real robotic systems.
A track record of taking research from hypothesis through implementation and robust validation.
About General Robotics
General Robotics is building the intelligence grid for physical AI — the platform that makes any robot, from robotic arms to humanoids, genuinely intelligent. Headquartered in Redmond, Washington, we're venture backed, including by Accenture, who invested in General Robotics in 2026 to advance Physical AI-powered robotics in manufacturing and logistics, and we're also part of Microsoft's Startups Pegasus Program. Our team's work spans some of the most widely adopted robotics and AI research to come out of Microsoft Research, Google Research and DeepMind — including AirSim, PACT, ClimaX, Tensorflow Object Detection and VideoPoet.
Our Singapore team works closely with the US team on GRID's research and development as an extended research facility, and to support our customers in the region. Engineers from both offices regularly visit each other for collaboration.

Source: Generalrobotics careers (Ashby)