ROLE Machine Learning Engineer - Robot Manipulation Design, implement, test and deploy robot manipulation algorithms for industrial applications

Maven Robotics, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Maven Robotics, Inc. in San Francisco is seeking a Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy RL/IL based manipulation algorithms for assembly and material handling.

You will translate objectives into ML problems and deploy scalable models into robotic systems. Join a fast-paced startup to integrate ML into robotic workflows, validate performance in simulated and real environments, and mentor junior engineers while pushing the boundaries of robotics

Qualifications

• MS or PhD in machine learning, CS, robotics or related field.
• Strong practical experience training and deploying ML models.
• Deep RL/IL understanding for robotics applications.
• Proficiency in Python and PyTorch.
• Experience with data collection and preprocessing for ML models.
• Self-starter with ability to plan and execute solutions.
• Interest in fast-paced startup environments and collaboration.

Responsibilities

• Design and implement ML algorithms for robotic manipulation.
• Translate objectives into ML problems and deploy scalable models.
• Integrate ML solutions into robotic workflows and real-world systems.
• Collaborate across disciplines and mentor junior engineers.
• Drive innovation with the latest ML research in robotics.
• Own ML projects from conception to deployment.

Skills

Reinforcement learning

Imitation learning

Python

PyTorch

Robotics knowledge

Problem solving

Mentorship

Education

MS or PhD in machine learning, computer science, robotics, or related field

Tools

Gazebo

MuJoCo

Stable Baselines

RLlib

GPU acceleration

Job description

Role Description

We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.

In this role you will:

• Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
• Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
• Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
• Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
• Take ownership of critical ML projects, seeing them through from conception to successful deployment.
• Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.

Must-have

• MS or PhD in machine learning, computer science, robotics, or a related field.
• Strong practical experience in training and deploying machine learning models for real-world applications.
• Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
• Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
• Experience with data collection, preprocessing, and management in the context of training ML models.
• Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
• Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have

• Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
• Experience in:
• Designing and implementing reward functions for complex manipulation tasks.
• Developing models that can handle noisy, incomplete, or sparse data.
• Deployment of ML models to edge devices for real-time inference.
• Accelerating ML training processes using GPU, TPU, or other HW accelerators.
• Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.
• General knowledge of robotics principles, including kinematics, dynamics, and control.
• Publications or contributions to the machine learning community, particularly in areas related to robotics or reinforcement learning.