Product Manager, Autonomous Fleet Technology

What You'll Do

• Own the Fleet Engineering roadmap; enabling the scaling of autonomous operations across Operations, Deployments, Commercial, Supply Chain, and Platform/Infra through one coherent, defensible plan.
• Enable Bedrock’s autonomous scope expansion through fleet generalization: streamline the end to end bring up of new autonomous machine models and excavator tools.
• Deliver the fleet tools & agents that move the needle: a real-time fleet readiness platform, a field app for provisioning, calibration, and acceptance testing, and the issue-routing agents that connect field operators to engineering and back.
• Drive the shift from manual, tribal-knowledge processes to authoritative data stores and event-driven automation—establishing one record per machine as the source of truth and automating the happy path so engineering is pulled in only for genuine exceptions.
• Define and instrument the metrics that matter—fleet readiness, time-in-state, MTTR, provisioning effort, parts-shortage lead time—and run the function off the data.
• Hold deployment reliability and partner SLAs as a first-class constraint while the fleet grows, partnering closely with Fleet Ops on staffing and reliability models.
• Own Voice-of-Customer for field technicians and operators—get on real job sites, understand the work before you spec tools for it, and turn what you learn into a roadmap.
• Operate with a relentless sense of ownership for the reliability and growth of the fleet you support.

What You'll Bring

• 8+ years in product management, ideally in operationally complex, physical-world environments—robotics, logistics, fleet, hardware, or industrial systems where software meets machines in the field.
• A track record of building internal tools and platforms that replaced manual processes and scaled a real operation, tied to measurable gains in reliability or throughput.
• Strong systems thinking: comfort designing around a single source of truth and event-driven workflows, and reasoning clearly about what breaks at 100x scale.
• Fluency with operational and reliability metrics—uptime, MTTR/MTBF, time-in-state, SLA design—and a data-driven approach to prioritization and cost tradeoffs.
• A bias toward lightweight structures and durable processes that help a growing company scale through complexity, and the hands-on, low-ego, field-aware instincts to make them stick.