Founding Research Engineer, World Models & Simulation

Orbifold AI, Inc.; Palo Alto (CA); Sur place; USD 150 000 - 210 000; Plein temps

Résumé du poste

Orbifold AI, Inc. is seeking a Research Engineer to develop and evaluate world models and simulation environments for robotic policies. You will work on learning-based simulators, targetted data generation, and scalable evaluation harnesses to stress-test policies in diverse scenarios.

The role emphasizes solid ML research with first-author publications or equivalent shipped work, plus strong engineering in PyTorch and distributed training within a startup-like research environment.

Qualifications

• PhD or equivalent research experience with first-author publications or shipped work in world models, video generation, or model-based RL.
• Strong ML engineering, PyTorch, distributed training experience demonstrated in at least one project.

Responsabilités

• Train learned world models on real-captured data and develop evaluation environments for policy testing.
• Generate synthetic data targeted to measured deficits to improve downstream policies.
• Build rollout and evaluation harnesses including scenario suites, scoring, and reproducible runs.
• Quantify realism gaps between models and the real world and drive data collection.
• Collaborate with teams using traditional simulators when appropriate, evaluating where learned models excel or falter.
• Reconstruct real sites as test environments for partner policies before live deployment.

Connaissances

PyTorch

Distributed training

ML engineering

Formation

PhD or equivalent research experience

Description du poste

Orbifold AI, Inc. is seeking a Research Engineer to develop and evaluate world models and simulation environments for robotic policies. You will work on learning-based simulators, targetted data generation, and scalable evaluation harnesses to stress-test policies in diverse scenarios.

The role emphasizes solid ML research with first-author publications or equivalent shipped work, plus strong engineering in PyTorch and distributed training within a startup-like research environment.