Data Annotation Lead

Physical Intelligence; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Physical Intelligence in San Francisco is seeking a Data Annotation Lead to own and scale annotation operations critical for model improvement. You will manage throughput, quality, cost, and delivery across various annotation types, expanding the team from hundreds to thousands.

The ideal candidate has over 7 years of experience in data operations, a track record of establishing effective annotation programs, and strong skills in cross-functional partnership with engineering and product teams. A commitment to quality and data-driven management is essential.

Qualifications

• 7+ years leading scaled data or annotation operations, including teams in the 100s+.
• 3+ years as a manager of managers.
• Deep command of annotation best practices.

Responsibilities

• Own annotation operations end-to-end, focusing on throughput, quality, cost, and delivery.
• Scale the annotation workforce from hundreds to thousands, including workforce planning.
• Define and continuously raise quality standards and operational metrics.

Skills

Leading scaled data operations

Data-driven management

Cross-functional partnership

Clear communication

Annotation best practices

Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically‑actuated devices of the future.

The role

We're looking for a Data Annotation Lead to own annotation operations and scale the team behind it. Annotation is core to how our models improve, and demand is growing fast. You will scale the annotation workforce from 100s to 1,000s while raising the quality bar — designing the org, the training pipeline, the quality system, and the metrics that let it scale efficiently.

You will own the people and the operation: throughput, quality, cost, and delivery across every annotation type.

In this role you will

• Own annotation operations end-to-end: throughput, quality, cost, and on-time delivery across all annotation types.
• Scale the annotation workforce from 100s to 1,000s: workforce planning, org design, and the hiring and onboarding funnel.
• Build and lead a multi-layer management structure; hire, develop, and manage managers and team leads.
• Scale throughput with autolabeling and model-based annotation: design human-in-the-loop workflows where models pre-label and annotators review, correct, and escalate, so output grows faster than headcount.
• Stand up the training and certification pipeline that brings new annotators and teams to the quality bar quickly and consistently.
• Define and continuously raise the quality bar: rubrics, calibration, audit/QA loops, and quality‑adjusted productivity.
• Establish operational metrics and reporting (presence, throughput, acceptance/rejection, rework) and drive week-over-week improvement.
• Run capacity planning and prioritization against competing demand; allocate teams to the highest-impact work.
• Manage performance at scale with clear standards, feedback, and a fair improvement/exit process.
• Partner with product and engineering to define annotation tooling that unlocks throughput and quality.
• Partner with research and project leads to translate annotation needs into clear instructions, rubrics, and SLAs.
• Own the in-house vs. vendor mix and manage external partners where used.
• Own the annotation operating budget and unit economics; improve cost-per-annotation while protecting quality.

What you'll bring

• 7+ years leading scaled data or annotation operations, including teams in the 100s+.
• 3+ years as a manager of managers.
• Track record standing up 0→1 annotation programs.
• Deep command of annotation best practices, operations, and strategy.
• Experience integrating autolabeling and model-based annotation into human workflows; building human-in-the-loop pipelines that raise throughput without sacrificing quality.
• Fluency with operational and quality metrics; data-driven management of large workforces.
• Strong cross-functional partnership with product, engineering, and research/ML.
• Clear written and verbal communication; able to set and hold standards across a large, distributed team.
• Working understanding of ML and why annotation quality drives model performance.

Nice to have

• Experience in robotics, autonomous vehicles, or frontier‑AI data pipelines.
• Experience managing distributed/global and/or vendor workforces.
• Built annotation tooling or partnered tightly with a tooling team.
• Experience training or fine-tuning autolabeling models, or partnering closely with the ML teams that do.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.