Machine Learning: World Models

The Bot Company

We're building a helpful robot for every home.

We're a small team of engineers, designers, and operators based in San Francisco. Our team comes from Tesla, Cruise, OpenAI, Google, Pixar, and many other great companies. In the past we've shipped to hundreds of millions of users and know what it takes to build amazing products and experiences.

Our team is deliberately lean to promote rapid decision making and do away with bureaucracy and hierarchy. Everyone is an IC and is empowered with massive scope, radical ownership, and direct responsibility. We work across the stack with a culture built for rapid iteration and fast execution.

What We Look for in All Candidates

All roles at The Bot Company demand extreme sharpness and the ability to move fast in high-intensity environments. Throughout the process, we expect candidates to demonstrate:

• Exceptional mental acuity: you think quickly, learn instantly, and reason across unfamiliar domains.
• Engineering curiosity: you naturally dig into how systems work, even outside your specialty.
• High performance mindset: you move fast, handle ambiguity, and excel when the environment is demanding.

About the role

We are building neural simulators that understand the "grammar" of the physical world, including physics, causality, and long-term dynamics.

You will develop video generation into controllable, large-scale world models.

What You'll Do

• Architect Neural Simulators: Design and train spatiotemporal models that move beyond short clips toward coherent, long-form world simulations.
• Scale Training: Own the end-to-end training of multi-billion parameter models on huge clusters.
• Own the Training Loop End-to-End: Design, run, debug, and iterate on large-scale training experiments-diagnosing failure modes, improving data mixtures, and tightening evaluation to drive measurable gains.

Requirements

• Very strong coding skills in Python, C++, or Rust.
• Video Generation Expertise: Deep experience shipping/researching high-fidelity video models.
• Architectural Intuition: Ability to design from scratch and reason about scaling laws and failure modes.
• Infrastructure Fluency: Comfortable managing and optimizing large-scale experiments on massive GPU clusters.