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.
This is a hands-on role for someone who thrives in
high-ambiguity, high-velocity environments and can bring structure and discipline to rapidly evolving labeling workflows
- What You Will Do -
Program Execution & Delivery
- Manage AI data labeling programs from scoping to delivery, ensuring high-quality annotations at scale.
- Translate Frontier Labs research needs into concrete annotation specs, rubrics, and task designs.
Own timelines, throughput plans, and quality controls for critical datasets used in LLM training and evaluation.
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Stakeholder Management
- Partner with researchers, data scientists, product, and ops to ensure labeling goals are aligned with model objectives.
- Work cross-functionally to drive task clarity, resolve ambiguity, and incorporate feedback into successive batches.
- Act as the single-threaded owner for specific labeling programs, managing internal and external partners.
Operational Infrastructure
- Develop and refine batching strategies, smart sampling plans, and audit workflows.
- Drive QA processes, including golden set calibration, rubric refinement, and disagreement adjudication.
- Ensure traceability from raw inputs to final labeled outputs, and track quality regressions over time.
Process Design & Automation
- Identify opportunities to apply model-in-the-loop labeling, active learning, or self-checking pipelines.
- Collaborate with tool owners and engineers to integrate annotation workflows with internal tooling systems.
Own feedback loops that enable raters to improve over time and reduce error variance
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- What You Will Need -
Bachelor's degree in Engineering, Data Science, Linguistics, or related technical/analytical field.
5+ years of program or project management experience in AI/ML, data ops, or labeling infrastructure.
Demonstrated ability to manage
end-to-end data pipelines in AI/ML or research environments.
Strong working knowledge of
Robotics, Physical AI Data labeling tasks, such as:
- Object detection and recognition
- Semantic & Instance Segmentation
- Depth & Pose Estimation
- Grasp Detection
- Action Segmentation
- Trajectory Labeling
- Prompt-response evaluation
- Instruction tuning
- Dialogue evaluation
- Vision-language QA
- Video slot tagging
- Image Tagging
- Documentation Extraction
- Data collection annotation
- HRI
Experience collaborating with research or model teams to scope data collection requirements.
Excellent written and verbal communication skills
- Preferred Qualifications -
- Experience in frontier AI research environments, such as foundation model labs or GenAI startups.
- Familiarity with tools like Label Studio, Scale AI, SuperAnnotate, Snorkel Flow, or in-house annotation platforms.
- Understanding of LLM training and evaluation lifecycles.
- Experience working with human-in-the-loop systems or model-assisted labeling pipelines.
- Familiarity with multilingual or multi-cultural annotation programs
