Dyna Robotics
Robotics
AIDataStrategist
“AI Data Strategist at Dyna Robotics. Skills: Data Strategy, AI/ML, Robotics, Model Improvement, Data Lifecycle Management. Define data requirements that drive model improvement across Dyna's robotics platform. Establish specifications, frameworks, and feedback loops that determine whether data improves models”
What You'll Achieve.
Define data requirements that drive model improvement; Establish specifications, frameworks, and feedback loops that determine whether data improves models; Translate real-world operational realities into high-impact data strategy decisions; Create a real-time, organization-wide view of data lifecycle health
Industry & Context.
Structured Problem Solver; Analytically Minded; Instincts for failure analysis, dataset structure, and feedback loops
What They're Looking For.
Must Have
4-8+ years of experience working in AI/ML, robotics, autonomy, or data-centric systems roles, Proven experience defining data quality standards, evaluation frameworks, annotation systems, or data strategy for machine learning products, Experience working closely with cross-functional teams, including ML researchers, operations, annotation teams, and engineering, A deep understanding of how deployment failures, edge cases, and real-world operational data translate into model training and evaluation improvements
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
What You'll Do.
Define data requirements that drive model improvement across Dyna's robotics platform
Establish specifications
and feedback loops that determine whether data improves models
Identify lifecycle gaps in data collection
Prioritize data collection efforts
Define how robot episodes should be labeled
Determine rubrics and taxonomies for meaningful signal capture
Define quality benchmarks for data
Translate field realities into data strategy decisions
Establish metrics for data pipeline health
Create organization-wide view of data lifecycle health
How You'll Work.
Team & Collaboration
Partner closely with the operations team; Act as a strategic consumer of operations output; Work closely with cross-functional teams, including ML researchers, operations, annotation teams, and engineering
Communication Scope
Written communication skills; Verbal communication skills
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