Job
Description
As a driven and detail-oriented L5B Program Manager at Frontier Labs AI, your role will involve leading the end-to-end execution of AI data labeling workflows across various data types like text, image, audio, video, and instruction-tuned datasets. You will partner closely with researchers, data scientists, product managers, and annotation vendors to ensure the accuracy, diversity, and alignment of data with evolving research needs. Your ability to thrive in high-ambiguity, high-velocity environments and bring structure to rapidly evolving labeling workflows will be crucial. **Key Responsibilities:** - Manage AI data labeling programs ensuring high-quality annotations at scale - Translate research needs into concrete annotation specs, rubrics, and task designs - Own timelines, throughput plans, and quality controls for critical datasets used in model training and evaluation - Partner with stakeholders to align labeling goals with model objectives - Drive task clarity, resolve ambiguity, and incorporate feedback into successive batches - Develop batching strategies, sampling plans, and audit workflows - Drive QA processes including golden set calibration and rubric refinement - Identify opportunities to apply model-in-the-loop labeling and automation pipelines - Collaborate with tool owners and engineers to integrate annotation workflows - Own feedback loops to enable raters to improve over time **Qualifications Required:** - 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 various physical AI data labeling tasks - Experience collaborating with research or model teams to scope data collection requirements - Excellent written and verbal communication skills In addition to the above, you should ideally have experience in frontier AI research environments, familiarity with annotation tools, understanding of LLM training and evaluation lifecycles, and experience with human-in-the-loop systems or model-assisted labeling pipelines. Familiarity with multilingual or multi-cultural annotation programs would also be beneficial for this role.,
