Machine Learning Scientist/Engineer

Overview

As a founding Research Scientist on the MEGA team, you will help build foundation models for general-purpose robotics, enabling robots to perceive, reason, and act in the real world. You’ll design architectures, learning objectives, and data strategies, and oversee end-to-end experiments from pre-training to deployment. You’ll collaborate across ML, robotics, and hardware teams to translate research into measurable robot performance and compelling demonstrations. This role offers ownership and impact in shaping scalable, multimodal AI for embodied agents.

Pay / Benefits

• salary benchmarked against market
• equity participation
• relocation support and visa sponsorship
• hybrid working with core hours
• learning and development budgets
• comprehensive benefits (health/dental, leave, retirement)

Responsibilities

• Design and run experiments on model architectures, objectives, and data strategies for robot foundation models
• Build scalable pre-training and post-training methods using diverse video and robot-interaction data
• Curate and evaluate large robotics datasets (egocentric, UMI, teleoperation)
• Develop distributed training pipelines for large multimodal models
• Collaborate with ML engineers, roboticists, and hardware teams to translate research into real-world performance
• Communicate findings through internal reviews, publications, and robot demonstrations
• Research and develop model architectures, learning objectives, and data strategies for robot foundation models
• Develop scalable self-supervised and generative pre-training methods using web and robot data
• Apply post-training methods including supervised fine-tuning, imitation learning, and RL to improve robot capabilities
• Curate, filter, and evaluate large-scale robotics datasets (egocentric, UMI, teleoperated)
• Build and use distributed training pipelines for large models and datasets
• Work closely with robotics and hardware teams to connect progress to measurable performance
• Communicate research through internal channels and publications

Key requirements

• Experience in machine learning with a focus on multimodal foundation models
• Experience with scalable training (multi-node, large datasets, large models)
• Strong research track record with publications in top venues (e.g., ICRA, CoRL, CVPR, NeurIPS, ICML, ICLR)
• Strong engineering skills with modern ML frameworks
• Ability to design and run rigorous experiments in collaboration with engineering and robotics teams
• Experience translating research ideas into working systems or deployed capabilities
• Strong communication skills for sharing research across teams

• strong communication
• collaboration across disciplines
• curiosity and drive for autonomy
• multimodal foundation models
• self-supervised and generative pre-training methods
• imitation learning and reinforcement learning

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