Manager, PgM Tech - Frontier Labs

About the Role

We are looking for a driven and detail-oriented L5B Program Manager to join our Frontier Labs AI team, focused on building high-quality, multi-modal data pipelines to support advanced model development and foundational research.

In this role, you will lead the end-to-end execution of AI data labeling workflows across text, image, audio, video, and instruction-tuned datasets, partnering closely with researchers, data scientists, product managers, and annotation vendors. You will play a critical role in scaling and operationalising labeling operations, ensuring that the data used to train and evaluate cutting-edge models is accurate, diverse, and aligned with evolving research needs.

1. Manage AI data labeling programs from scoping to delivery, ensuring high-quality annotations at scale.
2. Translate Frontier Labs research needs into concrete annotation specs, rubrics, and task designs.
3. Own timelines, throughput plans, and quality controls for critical datasets used in LLM training and evaluation.

1. Partner with researchers, data scientists, product, and ops to ensure labeling goals are aligned with model objectives.
2. Work cross-functionally to drive task clarity, resolve ambiguity, and incorporate feedback into successive batches.
3. Act as the single-threaded owner for specific labeling programs, managing internal and external partners.

1. Develop and refine batching strategies, smart sampling plans, and audit workflows.
2. Drive QA processes, including golden set calibration, rubric refinement, and disagreement adjudication.
3. Ensure traceability from raw inputs to final labeled outputs, and track quality regressions over time.

1. Identify opportunities to apply model-in-the-loop labeling, active learning, or self-checking pipelines.
2. Collaborate with tool owners and engineers to integrate annotation workflows with internal tooling systems.
3. Own feedback loops that enable raters to improve over time and reduce error variance

1. Object detection and recognition
2. Semantic & Instance Segmentation
3. Depth & Pose Estimation
4. Grasp Detection
5. Action Segmentation
6. Trajectory Labeling
7. Prompt-response evaluation
8. Instruction tuning
9. Dialogue evaluation
10. Vision-language QA
11. Video slot tagging
12. Image Tagging
13. Documentation Extraction
14. Data collection annotation
15. HRI

1. Experience in frontier AI research environments, such as foundation model labs or GenAI startups.
2. Familiarity with tools like Label Studio, Scale AI, SuperAnnotate, Snorkel Flow, or in-house annotation platforms.
3. Understanding of LLM training and evaluation lifecycles.
4. Experience working with human-in-the-loop systems or model-assisted labeling pipelines.
5. Familiarity with multilingual or multi-cultural annotation programs