Data Collection Manager

Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.
About the Role
We are seeking a Manager, Data Collection to lead and scale the team responsible for teleoperation data collection supporting robot learning at Agility Robotics.
This role owns the daily operating rhythm of data collection: operator staffing and training, rig and station readiness, campaign execution, vendor and contract labor management, and the throughput and data quality metrics that determine whether our AI teams get the data they need at the volume and quality they need it.
Data collection is the input to our learned policies. The quality of this operation directly gates model progress. We are looking for someone who treats it as a production operation with measurable output, not as a support function.
About the Work
Team Leadership & Talent
Build, train, and lead the data collection team, including robot operators and data collection specialists in Fremont.
Own hiring for the function: define the operator profile, build the interview process, and scale the team against collection demand.
Develop onboarding and ongoing training programs, including certification on teleoperation rigs, capture hardware, and task protocols, so that operator output is consistent across people and shifts.
Manage shift scheduling and coverage to match campaign demand, including extended hours or weekend coverage when program milestones require it.
Build a career path from data collection into system test, robot operations, and other technical roles across the organization.
Promote a culture of safety, including proper use of teleoperation and capture equipment, ergonomics, and adherence to safety procedures.
Data Collection Operations & Throughput
Own day-to-day readiness of teleoperation stations, capture rigs, robots, and workcells used for data collection, and coordinate with engineering and technical support on calibration, configuration, and return to service.
Plan and execute collection campaigns against the task taxonomy and volume targets set with AI Engineering, including scoping, scheduling, and progress reporting.
Identify bottlenecks through workflow analysis, time studies, and capacity modeling, then drive practical fixes.
Lead root cause analysis on recurring operational issues, including rig downtime, capture failures, and protocol drift, and follow through on corrective actions.
Create and maintain SOPs, work instructions, task protocols, and process controls, and manage change control when protocols or hardware are updated.
Support rollout of new capture hardware, sensors, and tooling in partnership with Engineering, Facilities, and Safety.
Data Quality & KPI Ownership
Own and report the core operating metrics for data collection: episode throughput per operator hour and per shift, usable episode yield and rejection rate, station utilization, rig uptime and downtime, cycle time, operator productivity, and cost per usable episode.
Define and enforce the quality bar in partnership with AI Engineering, including what makes an episode usable, and drive first-pass yield up over time.
Track coverage against the target task and scenario taxonomy so gaps are visible before they block model work.
Build dashboards and reporting that give AI Engineering and leadership live visibility into collection volume, quality, and capacity without having to ask.
Use operational data to drive continuous improvement in throughput, quality, and unit cost.
Vendor & Contract Labor Management
Own vendor and contract labor relationships supporting data collection, including sourcing, scoping statements of work, rate negotiation, onboarding, and offboarding.
Define and manage vendor performance against quality, throughput, and SLA commitments, and hold vendors accountable to the same metrics as internal staff.
Manage the mix of internal and contract capacity to flex with campaign demand while protecting data quality and continuity.
Evaluate external data collection service providers where in-house capacity is not the right answer, and make build versus buy recommendations with supporting cost and quality data.
Partnership with AI Engineering
Partner with AI, Perception, and Skills engineering teams to translate the model roadmap into concrete collection plans: which tasks, which scenarios, how many episodes, at what quality bar, by when.
Serve as the operational point of contact for new data collection projects, from scoping through delivery.
Close the loop with AI Engineering on which collected data actually improved policy performance, and use that to reprioritize collection rather than optimizing volume alone.
Translate engineering data requirements into operator-executable protocols, and translate operator-observed issues back into engineering terms.
About You
Experience
6+ years of experience in technical operations, data operations, manufacturing, industrial or process engineering, lab operations, or production environments.
3+ years of experience leading frontline or technical teams, including hiring, performance management, and mentoring.
Familiarity with teleoperation rigs, VR and XR capture setups, motion capture, or wearable sensor capture.
Experience running a metrics-driven operation, with accountability for throughput, quality, and utilization targets.
Experience managing vendors or contract labor, including performance and SLA management.
Experience operating and troubleshooting early-stage technology in a fast-moving environment with frequently changing priorities.
Preferred Qualifications
Experience establishing and growing a teleoperation or robot data collection function at another robotics, humanoid, autonomous vehicle, or embodied AI company.
Working understanding of imitation learning and robot learning data requirements, sufficient to hold a substantive conversation with AI engineers about what data is worth collecting.
Experience with Lean, Six Sigma, time studies, or similar process improvement methods.
Technical Expertise
Strong command of operating KPIs such as throughput, cycle time, takt time, utilization, yield, OEE, and uptime.