Robotics Machine Learning Engineer

Persona AI Inc•Houston, TX

23h

About The Position

Persona AI is seeking a Machine Learning Engineer to build and scale the machine learning infrastructure supporting humanoid robots working in heavy-industry environments. The role focuses on large-scale data pipelines, training infrastructure, and robot evaluation in simulation and on hardware. The engineer will manage data and training artifacts and develop production-level software across machine-learning components. The position contributes to Persona’s ML software stack across data, training, and evaluation; develops scalable infrastructure; and works with the ML team on plans for machine-learning model development. The role also involves cloud-based compute, data management, and task automation, maintaining awareness of ML research and development, and helping attract and grow the Machine Learning and Data teams. The role is full-time, based in Houston, Texas, and requires 10% travel.

Requirements

At least 3 years of experience in machine learning directly applied to robotics.
Bachelor’s, Master’s, or PhD in Robotics, Computer Science, or a related field.
Experience with production ML software deployment.
Experience using cloud computing to develop models and store data, such as AWS, Azure, or GCP.
Strong expertise with deep-learning frameworks such as PyTorch, JAX, or TensorFlow.
Ability to write high-quality software in Python, C++, or Rust.
Ability and willingness to tackle complex, large-scale robotics engineering problems.
Enthusiasm for collaborating in a fast-paced team environment.

Nice To Haves

Experience with other areas of ML applied to robotics, including robot simulation, locomotion, computer vision, robot hardware, and sensors.
Experience with software engineering practices and tools such as CI/CD, build systems, and Docker.
Experience training models and deploying them to robots in production environments.

Responsibilities

Contribute to Persona’s large-scale ML software stack across data, training, and evaluation.
Design and implement scalable ML software infrastructure with thoughtful abstraction.
Work with the ML team to develop and execute a comprehensive plan for machine-learning model development.
Develop test suites to ensure correct and performant execution of ML pipelines.
Use cloud platforms for flexible compute capacity, data management, and task automation.
Keep up to date with developments in ML research and practice.
Collaborate in attracting, nurturing, and growing the Machine Learning and Data teams.