Dyna Robotics
AI Data Strategist
1mo ago
USASeniordata strategymodel evaluationdata collectionquality frameworks
Senior individual contributor role focused on defining data strategies to improve AI models for robotics platform.
Responsibilities
- 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?
- 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.
Requirements
- 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.
Nice to have
- 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.
Other
- 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.
- 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.