Member of Technical Staff, Machine Learning

WHO ARE WE?

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.

WHO ARE WE 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.

The central challenge is making simulation a reliable guide to real-world decisions. You'll combine physical models with learning from real data, demonstrate when simulated improvements transfer to hardware, and build a data feedback loop in which each deployment improves our models and calibration methods. You'll also contribute to Antioch's longer-term approach to learned simulation, including world models and diffusion models, grounded in measurable customer outcomes.

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.

WHAT YOU'LL DO

- 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. - Build self-serve workflows with our platform team to ingest diverse sensor, actuator, and system telemetry, calibrate digital twins, and validate results reproducibly. - Establish a continuous data feedback loop: use deployment failures to guide data collection, improve models, and turn validated improvements into reusable platform capabilities. - Advance Antioch's learned simulation roadmap, including world models and diffusion models, through practical experiments and production integration. - Build and ship foundational ML models, pipelines, and tools as a core member of the team.