The posting
About the Opportunity
Our client, a well-funded, early-stage robotics and AI company is seeking a highly technical and execution-oriented Data Technical Product Manager (TPM) to own the end-to-end data infrastructure powering next-generation machine learning systems.
This role sits at the intersection of machine learning, data engineering, operations, and product management. You will be responsible for building and scaling the data flywheel that transforms raw data collected from deployed robotic systems into high-quality training datasets used to improve AI performance.
The ideal candidate has experience managing large-scale data operations, working closely with ML teams, and translating ambiguous technical requirements into structured execution plans.
This is a unique opportunity to join a rapidly growing robotics company building cutting-edge autonomous systems while working directly alongside world-class engineers, researchers, and founders.
What You'll Do
Own the End-to-End Data Platform
- Drive the roadmap for the central data platform.
- Manage the complete data lifecycle from capture through ingestion, storage, labeling, curation, and delivery to ML training pipelines.
- Partner with engineering teams to scale infrastructure supporting large-volume datasets.
Partner with Machine Learning Teams
- Translate ML research requirements into actionable data collection and annotation specifications.
- Define dataset requirements and collection strategies to support model development.
- Ensure researchers have access to reliable, high-quality training data.
Define Data Quality Standards
- Create QA frameworks, audit processes, and validation workflows.
- Establish standards for data quality, coverage, consistency, and labeling accuracy.
- Identify sensor drift, data degradation, and annotation issues before they impact training outcomes.
Manage Data Annotation Operations
- Source and manage third-party labeling vendors.
- Define vendor performance expectations and quality metrics.
- Conduct audits and implement continuous improvement initiatives.
Build Data Discovery & Infrastructure Capabilities
- Partner with infrastructure and platform engineers to improve:
- Data ingestion
- Cataloging
- Search
- Versioning
- Dataset management
Design the Data Flywheel
- Build systems that automatically surface edge cases and production failures.
- Create workflows that capture and route valuable operational data back into future training cycles.
- Improve data collection efficiency and model iteration speed.
Drive Metrics & Operational Excellence
- Define and monitor critical metrics including:
- Throughput
- Label quality
- Data coverage
- Dataset freshness
- Drift detection
- Operational efficiency
Required Qualifications
- 4+ years of experience in one or more of the following:
- Technical Product Management
- Data Engineering
- Large-Scale Data Operations
- Proven experience building and managing end-to-end data pipelines.
- Experience supporting applied machine learning or AI systems.
- Strong understanding of data quality management and governance.
- Ability to operate effectively in highly ambiguous, fast-moving startup environments.
- Experience coordinating across engineering, research, operations, and external vendors.
Preferred Qualifications
- Experience with multimodal datasets including:
- Video
- Sensor data
- Point clouds
- Telemetry
- Robotics, autonomous systems, computer vision, or industrial AI experience.
- Familiarity with large-scale data annotation workflows.
- Experience designing feedback loops that improve model performance over time.
