Engage directly with frontier lab research teams. You will be in the room during client scoping meetings — not as support staff, but as a technical peer. You’ll engage on methodology, challenge assumptions about data requirements, and shape project specifications based on a scientific understanding of how data composition affects model outcomes.
Develop deep scientific understanding of client engagements. For each project, you will build a working model of the client’s architecture, training methodology, and target capabilities. You’ll use this understanding to reason about why a particular data strategy will or won’t work, identify risks early, and iterate with empirical grounding — not intuition.
Run ablation studies and fine-tune open-weight models. You will fine-tune models on client data (and proxy data) to empirically measure the impact of our data on model performance. This is how we validate that what we deliver actually improves our customers’ models — and how we catch problems before the client does.
Consult on workflow and quality systems. You will partner with our Human Data Operations team to review annotation schemas, task designs, and quality rubrics before projects go into execution. Your job is to ensure the spec is technically sound — that the data we produce will actually serve the client’s training objectives.
Collaborate with Applied Research on publications and benchmarks. Our Applied Research team owns the long-horizon research agenda. Your role is to feed them signal from the field — generalizable findings, reusable methodologies, empirical results — and help drive joint projects to completion. You will contribute to benchmarks, white papers, and conference submissions that establish Labelbox’s research credibility.