# Member of Technical Staff, Machine Learning at Antioch

- Company: Antioch
- What the company does: 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.
- Company website: https://antioch.com/
- Type: Startups (AI role)
- Level: Senior
- Location: New York City
- Work setup: On-site
- Posted: 2026-10-26
- Apply by: 2026-12-10
- Apply: https://jobs.ashbyhq.com/antioch/d24db496-47db-4ef0-a287-f9ff6f376a34?utm_source=1752vc&utm_medium=careers
- Page: https://www.1752.vc/careers/jobs/antioch-member-of-technical-staff-machine-learning/

## 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

Tags: Engineering

Source: 1752vc Careers, https://www.1752.vc/careers/jobs/antioch-member-of-technical-staff-machine-learning/