Operations Manager, Robot Data Collection

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Robotics

This role manages the day-to-day operations for robot data collection, focusing on scaling the process, managing supervisors and operators, and ensuring high-quality data for robotics research. It involves setting targets, managing performance, driving retention, and bridging the gap between the operational floor and the research team.

What you'd actually do

  1. Run the operation: Own the day-to-day operation across all shifts. Set throughput targets, monitor adherence, debug when shifts under-deliver, and make weekly calls on staffing, scheduling, and task allocation
  2. Manage the team: You'll manage the supervisors who directly run our operators. Set clear goals, run a consistent performance loop, and grow the supervisory bench as shifts, and sites, expand
  3. Drive retention and engagement: Data collection is physical, repetitive, attention-heavy work. Design and continuously refine onboarding, training, performance incentives, recognition, and scheduling so operators stay engaged and the bar keeps rising. Approach this with a product mindset: hypothesize, ship, measure, iterate
  4. Own throughput and quality metrics. Set targets, build the dashboards, and run the review cadence that make performance visible across the team. Reduce reject rates, compress reset time, and increase usable hours per shift
  5. Plan capacity: Translate research priorities into concrete operational plans — operator allocation, supervisor coverage, equipment requirements, and risk. Forecast throughput, flag potential misses early, and re-plan as priorities evolve

Skills

Required

  • 4+ years of operations or program management experience
  • direct people management
  • A track record of running shift-based, throughput-driven operations
  • Demonstrated success reducing churn or improving retention in a frontline workforce
  • Comfort with metrics
  • Builder mindset
  • Strong written communication
  • Willingness to be on-site and on-floor

Nice to have

  • Experience managing managers or building a supervisor layer from scratch
  • Background in robotics, autonomous systems, or physical AI data collection
  • Experience scaling a frontline team from under 20 to 100+
  • Familiarity with workforce management software, shift-scheduling tooling, or QA workflow tools
  • Background in industrial engineering, operations research, or a related quantitative field

What the JD emphasized

  • high-quality robot data, collected by humans, at scale, day after day
  • builder role
  • processes, metrics, and programs that will scale it are still being shaped
  • track record of running shift-based, throughput-driven operations
  • Demonstrated success reducing churn or improving retention in a frontline workforce
  • Builder mindset