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Robotics Engineer

Turing
15 days ago
Full-time
Remote
Worldwide
Remote Engineering

About Turing

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.

Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com

This is a remote role, with travel required both within the US & internationally.

Overview

Turing is building the data layer for Physical AI, across four key pillars: (1) Synthetic Data (Sim), (2) Robot Teleoperation, (3) Human Motion Capture, and (4) Data Enrichment.

We are seeking a Robotics Engineer who has first-hand experience training foundation models for robotics. You will not just be an engineer; you will be the architect of our datasets. You know why models succeed or fail in the real world, and you will use that empirical knowledge to design high-value, off-the-shelf datasets that solve those failures for our customers.

You will act as the bridge between our data collection teams and our customers' model performance, eventually building out a "Benchmarking & Optimization" business unit that evaluates, diagnoses, and improves the performance of our customers’ robotics foundational models.

Why This Role?

  • Help Launch Business Unit: a 0-to-1 opportunity to help build a new business unit within Turing.
  • Define Industry Standards: Architect off-the-shelf datasets that will fuel the robotics ecosystem, setting the benchmark for Physical AI data quality.
  • Own the Evaluation Loop: Lead the critical feedback loop that validates data quality, proving empirically how specific