Product Manager - Data Engine

Applied Intuition•Sunnyvale, CA

2d•Onsite

About The Position

Applied Intuition is hiring a Senior Product Manager to make Data Engine a coherent platform for technical teams across its Cars, Trucks, Industrials, and Defense autonomy programs. The platform supports data workflows from ingestion through processing, discovery, enrichment, quality, and dataset delivery. The role’s first customers are Applied’s engineers and researchers, and the product manager will work directly with them to understand their workflows, find opportunities to replace duplicated efforts with shared capabilities, and establish a clear product strategy. The role also owns the platform’s external product roadmap, customer discovery, and packaging. In partnership with commercial teams, the product manager will determine which capabilities should be offered externally and what deployment, onboarding, and support capabilities customers need. The goal is to make important workflows easier, increase reuse across programs, and reduce reliance on bespoke integrations and engineering assistance. This is an in-office role. Full-time employees are expected to work primarily from an Applied Intuition office five days a week, with flexibility for occasional remote work and schedule adjustments.

Requirements

Bachelor’s or master’s degree in Computer Science or Electrical Engineering, or equivalent experience.
At least 4 years of Product Management experience at a top technology company or high-growth startup.
Experience owning complex technical products from discovery through delivery, adoption, and iteration, with senior-level responsibility for decisions and results.
Experience serving technical users through a shared platform, developer product, or data/ML product, including reconciling competing needs across multiple teams or customers.
Strong product discovery skills, including the ability to distinguish requested features from underlying problems and demonstrate how user evidence changed a product decision.
Sound platform judgment, including balancing reuse with specialization, setting support boundaries, and sequencing migrations without blocking users’ work.
Technical fluency in data flows, APIs, workflow orchestration, data quality, and production operations, with the ability to discuss architectural tradeoffs with engineers.

Nice To Haves

Experience with autonomy, robotics, sensor data, or other physical AI workflows.
Experience with ingestion, catalogs and lineage, data processing, ML data pipelines, or workflow orchestration.
Experience converting an internal platform into an external product or supporting customer-controlled and multi-region deployments.
Prior software engineering experience or hands-on work with technical users, APIs, and developer tooling.

Responsibilities

Set the vision, roadmap, and priorities for the end-to-end Data Engine product across multiple teams.
Define supported workflows, their users, and the requirements for a reliable production experience.
Observe user workflows, interview users and non-users, investigate workarounds, and use usage and support signals to inform decisions.
Communicate how user feedback was addressed and why product decisions were made.
Determine which capabilities belong in the shared platform, which require configuration or extension points, and which should remain program-specific.
Make explicit choices about what not to build.
Partner with design and engineering on APIs, interfaces, documentation, examples, onboarding, and migrations to drive adoption.
Help users discover existing capabilities and retire duplicated implementations when the shared alternative is ready.
Translate user needs into clear requirements and acceptance criteria, and make technical tradeoffs with engineering across reliability, data quality, latency, cost, compatibility, delivery speed, security, and data residency.
Lead external customer discovery, define a repeatable offering and its boundaries, and develop a commercial roadmap with sales and customer-facing partners.
Avoid turning individual customer requests into permanent product forks.
Track workflow success, onboarding time, adoption, support effort, and reuse.
Clearly communicate priorities, tradeoffs, and risks to program teams and leadership.