Robotics ML Engineer — Humanoid Robots for Industry

Persona AI; Houston (TX); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive compensation

Performance-based bonus

Medical benefits 99%

Equity

Paid time off

Winter break

Job summary

Persona AI in the United States is seeking a Robotics Machine Learning Engineer to shape the ML stack for our humanoid robot platform. You will join early and help design perception, manipulation, and locomotion capabilities, delivering production-ready models and robust pipelines.

The role emphasizes collaboration with universities and industry partners, deploying models in real world environments, and growing the ML and autonomy teams while operating in a fast-paced, high-stakes setting.

Qualifications

• 3+ years of ML for robotics.
• Experience with deep learning frameworks (PyTorch, JAX, TensorFlow).
• Cloud computing experience (AWS, Azure, GCP).
• Ability to deploy neural networks in production environments.
• Strong software engineering practices.

Responsibilities

• Design and develop the Persona ML software stack for manipulation, navigation, locomotion, and perception.
• Develop ML models and plan development with the team, keeping up with state-of-the-art research.
• Build data collection pipelines and ML deployment pipelines.
• Monitor real-world model performance and iterate.
• Collaborate with universities and industry partners to advance the platform.
• Contribute to growing the ML and autonomy teams.

Skills

ML for robotics

Deep learning

Cloud computing

Production deployment

Software engineering

First principles

Education

Advanced degree (MS/PhD) in CS/ robotics

Tools

PyTorch

TensorFlow

JAX

AWS

Azure

GCP

Job description

Persona AI in the United States is seeking a Robotics Machine Learning Engineer to shape the ML stack for our humanoid robot platform. You will join early and help design perception, manipulation, and locomotion capabilities, delivering production-ready models and robust pipelines.

The role emphasizes collaboration with universities and industry partners, deploying models in real world environments, and growing the ML and autonomy teams while operating in a fast-paced, high-stakes setting.