Data Technical Product Manager (Research & Data Platform)

Hiring: Data Technical Product Manager (Research & Data Platform)

📍 Location: San Francisco, California (On-site)

💼 Employment Type: Full-Time

💰 Compensation: $200,000+Base with Equity

🛂 Visa Sponsorship: Available

✈️ Travel Requirement: This position is primarily based on-site in San Francisco and requires candidates who are comfortable with occasional domestic and international travel (including Mexico) as business needs arise. Applicants should be excited to work in a highly collaborative, fast-paced environment with flexibility to travel when required.

We're hiring an exceptional Data Technical Product Manager (TPM) to own and scale the data platform powering cutting-edge autonomous robotic systems.

This is not a traditional Product Manager role.

You'll be responsible for designing and optimizing the complete data ecosystem that fuels AI models—from real-world robotic data collection to annotation, quality assurance, storage, governance, and delivery into machine learning pipelines.

Working directly with engineering leadership, ML researchers, infrastructure teams, and external data vendors, you'll become one of the key drivers behind the company's AI development lifecycle.

If you're passionate about solving complex technical challenges, building scalable systems, and working alongside some of the brightest minds in AI and robotics, we'd love to hear from you.

Why This Opportunity?

✔ Join a fast-growing robotics company building next-generation autonomous systems.

✔ Own one of the company's most business-critical platforms.

✔ Work directly with founders and senior engineering leadership.

✔ Build scalable infrastructure capable of handling petabytes of multimodal data.

✔ Solve complex challenges involving AI, machine learning, robotics, computer vision, and data engineering.

✔ Receive competitive compensation, meaningful equity, and the opportunity to shape products from the ground up.

Key Responsibilities

As the Data Technical Product Manager, you will:

• Own the complete lifecycle of ML training data—from robotic sensor capture through ingestion, storage, labeling, validation, and deployment.

• Build and execute the roadmap for the organization's centralized data platform.

• Partner with Machine Learning Researchers to translate model requirements into scalable data collection and annotation strategies.

• Collaborate closely with Data Engineers and Infrastructure teams to improve ingestion pipelines, storage architecture, cataloging, search capabilities, and data versioning.

• Design scalable data quality frameworks, validation pipelines, and audit processes to ensure high-quality datasets.

• Develop metrics for monitoring data quality, coverage, annotation accuracy, throughput, sensor drift, and operational efficiency.

• Build feedback loops that continuously capture production edge cases and improve future model performance.

• Lead relationships with third-party annotation and labeling vendors while ensuring quality standards and delivery timelines are consistently achieved.

• Coordinate cross-functional execution across Engineering, Research, Product, Operations, and external partners.

• Drive technical decision-making while maintaining project timelines across multiple concurrent initiatives.

Required Qualifications

We're looking for professionals who bring both technical depth and exceptional execution skills.

Experience

• 4+ years of experience in one or more of the following:

• Technical Product Management
• Technical Program Management
• Data Engineering
• Data Platform Engineering
• Machine Learning Infrastructure
• Large-scale Data Operations

Technical Skills

• Strong SQL proficiency

• Working knowledge of Python

• Experience with modern data infrastructure

• Understanding of ML data pipelines

• Experience working with large-scale structured and unstructured datasets

• Ability to inspect, query, validate, troubleshoot, and profile production datasets independently

• Experience building scalable operational workflows

• Knowledge of annotation pipelines, data governance, or data quality systems is highly preferred.

Ideal Candidate Profile

The ideal candidate is someone who:

✔ Has an engineering-first mindset.

✔ Enjoys solving operational and technical challenges.

✔ Can navigate ambiguity while creating scalable processes.

✔ Is highly organized and execution-oriented.

✔ Has exceptional stakeholder management skills.

✔ Can influence engineering teams without formal authority.

✔ Thrives in fast-paced startup environments.

✔ Is comfortable making data-driven decisions.

✔ Has experience managing cross-functional technical programs from concept through execution.

Preferred Industry Experience

Candidates with experience in any of the following industries are strongly encouraged to apply:

• Artificial Intelligence

• Robotics

• Autonomous Vehicles

• Computer Vision

• Defense Technology

• Embodied AI

• Machine Learning Infrastructure

• Data Annotation Platforms

• Large-scale AI Training Systems

Experience with multimodal datasets such as video, LiDAR, point clouds, sensor telemetry, or robotics data is highly desirable.

Preferred Company Background

We're particularly interested in candidates who have worked with organizations such as:

• Waymo

• OpenAI

• Anthropic

• Google DeepMind

• xAI

• Tesla Optimus

• Figure AI

• Physical Intelligence

• Zoox

• Aurora

• Cruise

• Applied Intuition

• Scale AI

• Labelbox

• Mercor

• Snorkel

• Invisible Technologies

• Palantir

• Anduril

• Shield AI

or other world-class AI, robotics, autonomous systems, or data infrastructure companies.

Education

• Bachelor's degree (or higher) in:

• Computer Science
• Software Engineering
• Data Science
• Computer Engineering
• Electrical Engineering
• Related Technical Discipline

What Success Looks Like

In this role, you'll build systems that enable AI models to learn from massive-scale, real-world data while ensuring accuracy, reliability, and operational excellence. Your work will directly influence how quickly machine learning teams can train, evaluate, and deploy intelligent robotic systems.

This is an opportunity to work on technology that sits at the intersection of robotics, AI, machine learning, and data engineering while making a measurable impact on the future of autonomous systems.

Ready to Build the Future?

If you're excited about building large-scale AI data platforms and solving some of the most challenging problems in robotics, we'd love to hear from you.

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