San Francisco, United States
Internship Explorer
Internship Explorer
As an Applied Research intern at Labelbox, you will design, build, and productionize evaluation and post‑training systems for frontier LLMs and multimodal models. You’ll own continuous, high-quality evals and benchmarks (reasoning, code, agent/tool‑use, long‑context, vision‑language, et al.), create and curate post‑training datasets (human + synthetic), and prototype RLHF/RLAIF/RLVR/RM/DPO‑style training loops to measure and improve real‑world task and agent performance. Build and own evaluation and benchmark suites for reasoning, code, agents, long‑context, and V/LLMs. Create post‑training datasets at scale: design preference/critique pipelines (human + synthetic), and target hard failures surfaced by evals. Experiment and prototype RLHF/RLAIF/RLVR/RM/DPO‑style training loops to improve real‑world task and agent performance. Land research in product: ship improvements into Labelbox workflows, services, and customer‑facing evaluation/quality features; quantify impact with customer and internal metrics. Engage with customer research teams: run pilots, co‑design benchmarks, and share practical findings through internal research reports, blog posts, talks, and published papers.
