<p>Role Overview</p> <p>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.</p> <p>Your Impact</p> <ul> <li>Build and own evaluation and benchmark suites for reasoning, code, agents, long-context, and V/LLMs. </li><li>Create post-training datasets at scale: design preference/critique pipelines (human + synthetic), and target hard failures surfaced by evals. </li><li>Experiment and prototype RLHF/RLAIF/RLVR/RM/DPO-style training loops to improve real-world task and agent performance. </li><li>Land research in product: ship improvements into Labelbox workflows, services, and customer-facing evaluation/quality features; quantify impact with customer and internal metrics. </li><li>Engage with customer research teams: run pilots, co-design benchmarks, and share practical findings through internal research reports, blog posts, talks, and published papers. </li></ul> <p>What You Bring</p> <ul> <li>A strong foundation in AI and machine learning, backed by a Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field (in progress degrees are acceptable for intern positions). </li><li>A deep understanding of frontier autoregressive and diffusion multimodal models, along with the human and synthetic data strategies needed to optimize them. </li><li>Passion and experience for LLM evaluation and benchmarking. </li><li>Expertise in training data quality construction, measurement and refinement. </li><li>The ability to bridge research and application by interpreting new findings and translating them into functional prototypes. </li><li>A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community. </li><li>Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow. </li><li>Exceptional communication and collaboration skills. </li></ul> <p>Applied Research at Labelbox</p> <p>At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advancing human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>