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.
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.
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").
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.

Requirements

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.

Benefits

Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
Competitive compensation
Flexible working hours

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

PythonPyTorchJAXImitation LearningReinforcement LearningVLA architecturesROS3D spatial mathematicsdata evaluation pipelinesrobotics data formats

Soft skills

cross-functional experiencecustomer engagemententrepreneurial mindsettechnical executionvalue communicationcollaborationproblem-solvingleadershipanalytical thinkingadaptability