Operations Manager, Robot Data Collection

We're building autonomous robotic systems that serve as the intelligent physical infrastructure of our Scientific Superintelligence Platform at Lila. To support our robotics research roadmap, we need to scale robot data collection to produce high-quality robot data, collected by humans, at scale, day after day while improving per-operator throughput, lowering churn, and keeping data quality consistent across every supervisor and shift

We're hiring an Operations Manager to own that scale-up end-to-end.

As Data Operations Manager, you'll own the day-to-day function that generates the training data powering Lila's robotic systems. That means managing the supervisors who run our data collection shifts, setting the conditions for consistent operator performance, and making sure every hour of work on the floor translates into high-quality, usable data for our research team.

This is a builder role. The team is early — a growing group of operators across two shifts — and the processes, metrics, and programs that will scale it are still being shaped. You'll design and own them. You'll report directly to the Robotics team and serve as the primary operational link between the floor and Robotics Research.

• 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
• 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
• 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
• 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
• 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
• Scale the footprint: Partner with facilities, recruiting, and leadership to expand the operation across additional shifts and, in time, additional sites. Maintain an operational playbook that makes opening each new shift or site repeatable rather than bespoke
• Be the bridge to research: Meet with the research team to understand upcoming priorities: which tasks, which embodiments, what success looks like. Translate those needs into shift plans, and surface friction (poor reset ergonomics, ambiguous instructions, unreliable hardware) before it costs the team hours

• 4+ years of operations or program management experience, including direct people management
• A track record of running shift-based, throughput-driven operations — ****warehousing, manufacturing, contact centers, data labeling, clinical ops, field operations or comparable
• Demonstrated success reducing churn or improving retention in a frontline workforce: We want to hear the specifics: the baseline, the intervention, and the outcome
• Comfort with metrics: You should be able to look at our shift summary, immediately spot what's interesting, and know what to ask for next
• Builder mindset: This is a function being built, not inherited. Some processes are still taking shape, and we are looking for the right person who sees that as an opportunity to grow and deliver
• Strong written communication — ****you will be the connection between the floor and research and engineering teams, and much of that work happens in documentation
• Willingness to be on-site and on-floor: This role depends on presence and proximity to the team

• 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

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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