Machine Learning Engineer / Machine Learning Researcher

We’re partnering with a well-funded, early-stage AI robotics company building a next-generation intelligent system designed for real-world home environments.

The company is backed by top-tier investors and founded by an exceptional group of technical leaders and operators. The team includes alumni from leading autonomy companies, frontier AI labs, and world-class technology and creative organizations. Collectively, they have shipped products used by hundreds of millions of people.

The team is intentionally small, flat, and execution-driven. There is no bureaucracy and no separation between research and engineering — everyone operates as a high-ownership individual contributor with broad scope and direct responsibility.

The Opportunity

Our client is hiring Machine Learning Researchers and Machine Learning Engineers to work across the full ML and robotics stack.

This role sits at the intersection of:

• Pre-training and post-training of large-scale models
• Reinforcement learning for multimodal foundation models
• World models and long-horizon prediction
• Video and temporal generative models
• ML infrastructure and data infrastructure for large-scale training

The work directly impacts models deployed on physical systems operating in real homes.

You will take ideas from research concept through deployment, building models that improve task understanding, visual memory, navigation safety, and personalization.

What You’ll Be Doing

• Designing and implementing state-of-the-art ML models and algorithms for embodied intelligence
• Developing highly data-efficient learning approaches from large-scale curated datasets and expert demonstrations
• Contributing to both offline training and online adaptation methods
• Working across distributed training systems and large-scale infrastructure
• Collaborating cross-functionally to integrate new approaches into the full autonomy stack

This is not a siloed research position — it is a hands-on, research-to-production role.

Who Thrives Here

The company operates in a high-intensity, high-expectation environment. Successful candidates typically demonstrate:

• Exceptional cognitive sharpness
• Strong engineering curiosity beyond their immediate specialty
• Comfort with ambiguity and rapid iteration
• A bias toward execution and ownership

Ideal Background

• Frontier AI labs
• Promising AI startups working on multimodal or foundation models
• Research teams within large technology companies
• Robotics, autonomy, or simulation-heavy environments

Advanced degrees are common within the team, but demonstrable depth and impact matter more than credentials alone.