QA Lead

Instawork is on a mission to create meaningful economic opportunities for skilled hourly professionals in communities around the globe. Our AI-powered labor marketplace helps local businesses scale, and enables global technology companies to push the frontiers of robotics and AI. Backed by world-class investors like Benchmark, Spark Capital, Craft Ventures, Greylock, Y Combinator, and others, we’re looking for exceptional talent to reimagine the way the world works.

About IRL (Instawork Robotics Labs) Researchers at UC Berkeley have identified a “100,000-year data gap” - the gulf between what trained AI language models and what physical robots actually have to learn from. Closing that gap is the defining infrastructure challenge of the physical AI era. IRL is Instawork’s answer to it. We deploy skilled workers into real commercial and residential environments - kitchens, warehouses, hotel floors, homes - to capture the high-fidelity task data that the world’s leading Robotics labs use to train their foundation models. Read more here.

• Lead Workforce & Operations: Drive day-to-day workforce management for Clippers, Graders, Regraders, SMEs, and ICs across shifts/sites, optimizing staffing, capacity planning, and throughput against SLA targets.

• Implement Quality Systems & Methods: Maintain the QMS and apply structured methods (RCA, CAPA, FMEA, 8D, SPC) to address recurring defects, calibration drift, gold-sample injections, and consensus checks.

• Own SOPs & Process Standards: Build, version-control, and continuously iterate SOPs for existing and new program launches, ensuring floor adoption and structured QA onboarding/re-calibration paths.

• Bridge Cross-Functional Teams: Translate engineering data requirements into floor-executable QA criteria, report field issues back with evidence, and represent QA in program launches and crisis calls.

• Drive Data-Led Quality: Monitor and analyze key QA metrics—defect/reject rates, inter-grader agreement (kappa), gold-sample pass rates, yield, and escalation volume—to root out quality degradation with data.

• Distributed QA/QC Leadership: Proven experience managing large or dispersed grading/QA teams across multiple shifts or sites.

• Quality Methods Depth: Strong working command and practical application of RCA, CAPA, FMEA, 8D, SPC, and QMS frameworks.

• Analytical & Data-Driven Mindset: Proven track record of building and interpreting quality dashboards, interrogating metrics, and quantifying operational trade-offs.

• Stakeholder & Communication Skills: Clear, structured communication skills across all levels—upward to leadership, cross-functionally