AI Data Strategist

Join us to shape the next frontier of AI-driven robotics! 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.

The Role 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?

What You'll Do Define Data Collection Priorities Identify lifecycle gaps: Maintain a clear, comprehensive view of where the data lifecycle has gaps, from pre-training through post-training. Direct collection efforts: Prioritize what the data collection team should focus on next, clearly distinguishing between data that merely adds volume and data that actually drives model performance. Design Evaluation & Quality Frameworks Set the standard: Define how robot episodes should be labeled and determine what rubrics and taxonomies capture meaningful signal. Establish quality benchmarks: Define what "good data" looks like for each task and model stage so the labeling team can execute flawlessly against your standards. Extract Signal from Operations Translate field realities: Partner closely with the operations team to understand what is happening in the field, including shift handoffs, collection quality, and deployment issues. Inform data strategy: Act as a strategic consumer of operations output, translating real-world operational realities into high-impact data strategy decisions without directly managing the operations team. Build Data Lifecycle Observability 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.