Robotics Software Engineer Vision-Language-Action Models

Trener Robotics; San Jose (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Trener Robotics in San Jose, CA, and Trondheim, Norway, is hiring a Robotics Software Engineer specializing in robot learning and manipulation. You will design, train, validate, and deploy contact‑rich manipulation skills on real robots, integrating vision, force‑torque, and tactile feedback while managing the full model lifecycle in a lab environment.

You will spend significant time in the lab running hardware experiments, curating datasets, and refining policies for reliable deployment on

Qualifications

• Master's degree or PhD in robotics, computer science, machine learning, or a related field, or equivalent professional experience in robot learning and manipulation.
• Proven experience shipping production-quality Python/C++ code.
• Deep expertise with modern ML frameworks (PyTorch preferred; JAX or TensorFlow welcome).
• Direct experience testing and iterating on robots with vision, force‑torque, and tactile sensing.

Responsibilities

• Develop, train, and deploy multimodal VLA and learned manipulation policies that solve contact‑rich tasks, integrating sensor‑based control strategies.
• Run the manipulation model lifecycle with regular trials on real hardware and curate high‑quality training datasets.
• Architect and build modular components for imitation and reinforcement learning to improve robustness of contact‑rich tasks.
• Build pipelines for continual learning from new demonstrations and deployment data.
• Stress‑test and optimize the real‑time execution framework for learned policies on robot hardware.

Skills

Python

C++

Robot learning

Imitation learning

Reinforcement learning

Vision-language

English fluency

Education

Master's or PhD in robotics/CS

Tools

PyTorch

ROS 2

MuJoCo

Isaac Sim

Drake

Job description

Location

Trondheim, Norway or San Jose, California, US.

Role

As a Robotics Software Engineer specializing in robot learning and manipulation, you will drive the design, training, validation, and deployment of contact‑rich manipulation skills on real robots. Your focus will be on solving dexterous, contact‑rich manipulation tasks. You will develop and integrate sensor‑based control strategies and Vision‑Language‑Action (VLA) policies, managing the full model lifecycle, from architecture and large‑scale data collection through training, validation, and reliable deployment on industrial and collaborative robots.

This is a hands‑on engineering role. You will spend significant time in the lab, running trials on hardware, curating high‑quality datasets, and getting models to work on real robotic systems.

How you'll move the mission forward

• Develop, train, and deploy multimodal VLA and learned manipulation policies that solve contact‑rich tasks, integrating sensor‑based control strategies that combine vision, force‑torque, and tactile feedback.
• Run the manipulation model lifecycle, conducting regular trials on real hardware to evaluate algorithmic changes and curate high‑quality training datasets.
• Architect and build modular components for imitation and reinforcement learning, continuously improving the robustness of contact‑rich tasks.
• Build pipelines for continual learning, enabling policies to keep improving from new demonstrations and real‑world deployment data over time.
• Stress‑test and optimize the real‑time execution framework for learned policies running on the robot.

Skills you will need to be successful

• A Master’s or PhD degree in robotics, computer science, machine learning, or a related field, or equivalent professional experience in robot learning and manipulation (e.g. visuomotor policy learning, imitation learning, applied reinforcement learning, Vision‑Language‑Action models).
• Professional experience in Python and C++, with a proven track record of shipping production‑quality code.
• Deep expertise with a modern ML framework (PyTorch preferred; JAX or TensorFlow also welcome).
• Direct experience testing and iterating on physical robots integrated with vision, force‑torque, and tactile sensors.
• Experience with robotic simulation environments (MuJoCo, Isaac Sim, Drake, or similar).
• You thrive in the lab, spending significant time running hardware experiments and troubleshooting real‑world edge cases.
• Business fluency in English.

Skills that will differentiate your candidacy

• Experience manipulating and analyzing complex, large‑scale, high‑dimensional multimodal data from varying sources, with proficiency training models at scale on cloud‑based workflows.
• Experience with edge deployment and model optimization for real‑time control: low‑latency inference, quantization, distillation, and pruning to meet tight control‑loop rates on resource‑constrained, on‑robot hardware and accelerators.
• Contributions to robot learning through publications or open‑source projects.
• Familiarity with ROS 2 and hands‑on experience using industrial and collaborative robots from vendors such as Universal Robots, ABB, Fanuc, and KUKA.
• Experience with multibody dynamics simulators such as Adams.