AI Data Strategist

Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Our frontier model has the top generalization and performance in the industry.

We are hiring an AI Data Strategist to define the data requirements that drive model improvement across Dyna's robotics platform.

This is a senior individual contributor role that focuses on strategy rather than managing operational execution. Instead of running the day-to-day data pipeline, you will define what operations and research execute against. You will establish the specifications, frameworks, and feedback loops that determine whether our data actually improves our models.

The core question you will help answer every week is: our model failed here, so what does that mean for our data strategy?

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• Define health metrics: Establish the metrics that measure the health of each phase of the data pipeline, including collection coverage, label quality, evaluation consistency, and model feedback loops.

• Drive visibility: Create a real-time, organization-wide view of data lifecycle health.

• Systems Thinker: You understand that superior models come from exceptional data strategy, not just massive data volume.

• Structured Problem Solver: Highly analytical and detail-oriented, with the ability to translate messy, real-world failures into structured frameworks.

• Analytically Minded: Possess strong instincts for failure analysis, dataset structure, and the feedback loops between deployment and training.

• Cross-Functional Influencer: Able to rally and influence cross-functional teams without needing direct authority.

• Clear Communicator: Strong written and verbal communication skills, with the ability to prioritize effectively in fast-moving environments where everything feels urgent.

• Core Experience: 4-8+ years of experience working in AI/ML, robotics, autonomy, or data-centric systems roles.

• Data Strategy Expertise: Proven experience defining data quality standards, evaluation frameworks, annotation systems, or data strategy for machine learning products.

• Collaborative Track Record: Experience working closely with cross-functional teams, including ML researchers, operations, annotation teams, and engineering.

• Edge-Case Proficiency: A deep understanding of how deployment failures, edge cases, and real-world operational data translate into model training and evaluation improvements.

• Experience operating in fast-moving, ambiguous startup or R&D-heavy environments

• Experience with embodied AI, video, or time-series data.

• Familiarity with evaluation pipelines, active learning, or data-centric AI.

• Exposure to annotation tooling such as Labelbox, Scale, CVAT, Encord, or Voxel51.