Member of Technical Staff, Simulation

Location: New York City, NY | Type: Full time, onsite | Team: Engineering

Who are we?

Antioch is the simulation platform for physical AI. Teams use us to develop, evaluate, and generate synthetic data for physical systems in high-fidelity cloud simulation, accelerated by 3D-native agents. We build precision-calibrated digital twins and run massively parallel simulation scenarios, so customers spend less time on expensive, slow real-world testing. Our customers include leading robotics, autonomy, and perception teams.

Who are we looking for?

Simulation engineers who will bring real customer systems into Antioch, push platform fidelity, and build the agentic harness that automates simulation. This is an early, high-ownership builder role. You'll turn ambiguous, incomplete real-world systems into validated simulations that customers can trust, working across physics, sensors, data, and tooling, often in tight loops with customers.

What you'll do

• Partner with customers to bring their robots, sensors, actuators, and environments into Antioch, turning messy specs and real-world data into working simulation models.
• Build simulation capabilities across the stack: dynamics, contacts, actuators, environments, and especially sensors.
• Build and calibrate high-fidelity models of cameras, lidar, radar, infrared, and UV, including noise, failure modes, and full-pipeline behavior.
• Make models fast and scalable: profile bottlenecks, cut compute cost, and improve determinism.
• Build Python tooling for calibration, testing, and customer onboarding.
• Build simulation bindings for our agentic framework, enabling automated digital twin creation and scenario testing.
• Explain assumptions, tradeoffs, and limitations clearly to customers and internal teams.

What we're looking for

• Hands-on experience with robotics or hardware simulation (Isaac Sim/Lab, MuJoCo, Gazebo, PyBullet, or similar). Isaac Sim/Lab is a plus.
• Experience building, calibrating, and validating simulations of one or more sensing modalities, including the underlying physics and signal processing.
• Ability to port complex physical systems into simulation and reason about sim-to-real and real-to-sim fidelity.
• Strong Python skills for data analysis, automation, and tooling.
• Comfort working from incomplete information, using measurement and system identification to fill gaps and documenting assumptions.
• A track record of shipping production-ready work, not just prototypes.
• Nice to have: non-visible sensing experience (IR, thermal, UV, radar, lidar), custom measurement or calibration software, computer vision background, Rust or C++.