High-fidelity simulation for Physical AI, at cloud scale. Join teams using Antioch to ship autonomous systems with confidence, entirely in simulation. Backed by Greylock.
About the role
Measure sim-to-real and real-to-sim gaps across components and complete systems, with repeatable benchmarks, held-out validation, and clear estimates of uncertainty. Develop and ship calibration methods with our simulation team, combining physical models, system identification, and ML to improve fidelity against real-world data. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- We're hiring a Member of Technical Staff, Machine Learning to be a core contributor to Antioch's ML team. You'll build the methods that make our simulations faithful to physical systems, and help drive the evolution from classical to learned simulation
- This is a hands-on technical role. You'll spend your time writing code, developing models, designing experiments, and shipping production capabilities alongside our simulation and core platform teams
- If you like the 0 → 1 grind and want your work to directly determine whether autonomous systems succeed in the real world, you'll fit right in
- Deep applied ML experience, taking ambiguous problems from experiments to reliable production systems
- Strong foundations in statistics, optimization, and experimental design, with sound judgment about uncertainty, data leakage, and generalization
More about this role
Antioch is the simulation platform for robotics and autonomy. We allow teams to develop, evaluate, and generate synthetic data for their physical systems in high-fidelity cloud simulation, accelerated by 3D-native agents.
With Antioch, teams create precision-calibrated digital twins, generate testing environments, and run massively parallel simulation scenarios. By moving development out of the field and into software, Antioch drastically reduces dependence on expensive, slow, and incomplete real-world testing.
We work with leading robotics, autonomy, and perception teams to make the deployment of physical autonomous systems safer, faster, and more cost-effective.
We're hiring a Member of Technical Staff, Machine Learning to be a core contributor to Antioch's ML team. You'll build the methods that make our simulations faithful to physical systems, and help drive the evolution from classical to learned simulation.
This is a hands-on technical role. You'll spend your time writing code, developing models, designing experiments, and shipping production capabilities alongside our simulation and core platform teams.
The central challenge is making simulation a reliable guide to...
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