Robotics ML Engineer, Manipulation & Edge Deployment

High Power Media Ltd; Northern (KY); Hybrid; USD 110,000 - 170,000; Full time

Job summary

High Power Media Ltd in the United States is seeking an ML Engineer, Manipulation to develop learning-based manipulation systems for mobile robots operating in dynamic human environments. You will build perception-to-action models, datasets, evaluation tooling, and deployment pipelines to improve robustness and safety.

Collaborate with AI platform teams, optimize for edge hardware, and drive end-to-end performance on real robots across sites.

Qualifications

• 3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential models.
• Strong proficiency in PyTorch and in building training/evaluation pipelines.
• Strong Python software development skills with collaboration across ML and robotics teams.

Responsibilities

• Develop learning-based manipulation models for end-to-end sensor-driven interaction in dynamic environments.
• Build and maintain manipulation training pipelines from robot logs and teleoperation.
• Design evaluation metrics and tests for manipulation reliability, safety, and recovery in real-world settings.
• Develop sim-to-real workflows, including simulation environments, domain randomization, and failure-mode testing.
• Optimize and distill models for edge deployment, benchmarking latency and memory.
• Collaborate with AI platform team to integrate policies with control and safety systems.
• Analyze field performance and drive iterative improvements through data collection.

Skills

Robotics ML

Manipulation

PyTorch

Python

Edge deployment

Collaboration

Education

Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering

PhD a plus

Tools

Isaac Sim

MuJoCo

ONNX/TensorRT

Git

Job description

High Power Media Ltd in the United States is seeking an ML Engineer, Manipulation to develop learning-based manipulation systems for mobile robots operating in dynamic human environments. You will build perception-to-action models, datasets, evaluation tooling, and deployment pipelines to improve robustness and safety.

Collaborate with AI platform teams, optimize for edge hardware, and drive end-to-end performance on real robots across sites.