Robotics Engineer

Location: Boston, MA (Hybrid)

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 attributes drive model performance.

Key Responsibilities

1. Data Strategy & Productization (Short-Term Focus)

• Design "Off-the-Shelf" Solutions: Leverage your experience to define specifications for high-value dataset products (e.g., "The definitive bi-manual manipulation dataset for household objects"). You will dictate the requirements to our Sim, Teleop, and Motion Capture teams to ensure they capture the right data.
• Commercial Engagement: Partner with the Sales team as a key technical authority. You will translate customer pain points (e.g., "Our model fails on transparent objects") into well defined data solutions.
• Quality Definition: Move beyond basic QA (resolution, frame rate) to semantic QA. Define what "high-quality" data looks like for model learning.

2. Model Evaluation & Gap Analysis (The Technical Core)

• Build the "Eval" Loop: Establish workflows to train and fine-tune open-source Robotics Foundation Models (e.g., OpenVLA, Octo, RT-X) on our data. You will empirically prove that Turing’s data improves model performance.
• Agentic Gap Analysis: Develop automated workflows to analyze datasets for coverage gaps. Use this analysis to provide high-value insights to customers (e.g., "Your model is failing because your training distribution lacks diverse lighting conditions—here is the synthetic data to fix it").

3. The Future: "End-of-Cycle" Optimization (Long-Term Focus)

• Benchmark-as-a-Service: Lead the development of virtual and physical benchmarking environments to test customer policies.
• The Optimization Loop: Build a full-cycle service where Turing: (1) Benchmarks a client's model, (2) Identifies performance gaps, (3) Translates gaps into data requirements, and (4) Delivers the data to close the loop.
• Establish a New Business Unit: Scale these capabilities into a dedicated "Benchmarking & Optimization" business unit. You will help build this new pillar, recruiting the team, defining the product solution, and driving the monetization strategy.

Required Qualifications

• 8+ years of total experience as a seasoned technologist with cross-functional operational experience.
• 5+ years in Robotics Learning with deep hands-on experience training and deploying machine learning models for robotics (Imitation Learning, RL, or VLA architectures). 
• 3+ Years of cross-functional & commercial experience in which you have "bridged the gap" between research and product sales (e.g., Applied Scientist, Technical Lead, or Solutions Architect). 
• Expert proficiency in Python. You must be capable of writing production-grade ML code, custom data loaders, and evaluation pipelines from scratch.
• Deep proficiency in PyTorch or JAX. Experience implementing and debugging complex deep learning architectures.
• Direct hands-on experience training, fine-tuning, or evaluating advanced robotics policies. Specifically, familiarity with Diffusion Policies or VLA architectures.
• Proficiency with ROS  (Robot Operating System). You must understand message passing, node architecture, and how to interface with hardware drivers.
• Experience manipulating standard robotics data formats. You understand the structure of "trajectory" data (state-action pairs).
• Strong grasp of 3D spatial mathematics (quaternions, coordinate frames, transformations) essential for manipulating robot kinematics and camera data.
• Entrepreneurial mindset required: ability to own end-to-end solutions, including technical execution, customer engagement, cost/ROI tradeoff analysis, and value communication to technical and non-technical stakeholders.

Preferred Qualifications

• You know exactly what makes a dataset valuable when building advanced robotics foundational models.
• You are comfortable explaining technical constraints to Sales teams, Product Managers, or external clients.
• Experience with NVIDIA Isaac Sim, Unity, MuJoCo, or Genesis. Ideally, you have built custom benchmark environments or generated synthetic datasets.
• Experience setting up distributed training workflows on cloud clusters and using experiment tracking tools like Weights & Biases (W&B).
• Experience using Vision-Language Models (VLMs) and other tools to automatically label or annotate large datasets.
• Ability to read and debug C++ code is a plus (useful for troubleshooting hardware drivers or legacy ROS nodes), but production coding in C++ is not required.
• Experience deploying models to edge compute devices (e.g., NVIDIA Jetson) or directly onto robot controllers.

Compensation: $156,000 to $202,500 + Equity