Senior Machine Learning Engineer

About the role

ML Engineer — Physical AI | Bay Area

About the Company

We partnered with a start up that is building the intelligence layer for the physical world.

While most AI today understands what things look like, this team is building systems that understand what things are at a molecular level.

The company has developed proprietary sensing technology capable of capturing, paired with large-scale world models that encode the physics and chemistry of real-world materials.

The company is bridging the gap between artificial intelligence and the physical world — giving machines the ability to perceive, reason about, and interact with their environment at a depth that has never been possible before.

Backed by leading venture firms, the team is assembling a world-class group of researchers and engineers to work on problems that sit at the frontier of what AI can do.

What the Company Is Building

Large world models that understand the physics and chemistry of real-world materials — not just pixels

A proprietary sensing platform that reads the molecular fingerprint of physical matter across thousands of spectral bands

Perception systems that enable machines to understand what they're touching, seeing, and interacting with at a chemical level

AI infrastructure that maps the material composition of the physical world in unprecedented detail

Real-world applications spanning robotics, earth observation, industrial sensing, and beyond

What the Role Involves

Designing, training, and scaling foundation models for physical-world understanding — working across spectral, spatial, and temporal data modalities

Developing and iterating on model architectures, training pipelines, and evaluation frameworks tailored to physical sensing data

Building production-grade ML systems that operate on real-world data at scale — not just benchmarks

Collaborating directly with hardware and sensing teams to close the loop between perception and intelligence

Pushing the boundaries of what world models can represent — materials, chemistry, physics, and beyond

What the Team Is Looking For

Strong experience in ML research or research engineering, with a focus on training and scaling models (LLMs, vision models, world models, or multimodal systems)

A track record of shipping ML systems into production and not just publishing papers

Comfort working across the full stack: data pipelines, training, evaluation, and deployment

Intellectual curiosity and genuine excitement about applying AI to the physical world

Why This Opportunity

Frontier problem space: The role offers the chance to work on AI systems that perceive the world in ways no existing technology can — genuinely new ground.

Hardware + AI integration: This is not a pure software play. The work sits at the intersection of proprietary sensing hardware and large-scale ML — a rare combination.

Small, exceptional team: Early-stage, talent-dense, and moving fast. Individual contributions will directly shape the product and the company.

Real-world impact: The systems built here will power applications across robotics, environmental monitoring, agriculture, materials science, and more.

Top-tier backing: Well-funded by leading venture investors with a clear path to scale.

If this sounds interesting, reach out, and find out more.