Applied Research Engineer, Agents

Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. Role Overview As an Applied Research Engineer at Labelbox, you’ll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work—browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You’ll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox’s strategy for collecting, synthesizing, and evaluating it. You will possess expertise in LLM agents and planning/execution loops, plus creativity in tackling problems across data design, interaction, and measurement. You’ll publish meaningful results, collaborate with customer researchers in frontier AI labs, and turn prototypes into reliable, scalable features. Your Impact Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities. Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. Develop data pipelines from diverse sources like code repositories, web browsers, and computer systems. Implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. Engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices. Collaborate closely with frontier AI lab customers to understand requirements and guide model development. Publish research findings in academic journals, conferences, and blog posts. What You Bring Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or related field. At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers. Experience building and training autonomous agents—tool use, structured outputs, multi-step planning—across browsers/GUI, codebases, and databases using SFT and RL. Constructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER). Adept at interpreting research literature and quickly turning new ideas into prototypes. Deep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their huma ... (truncated, view full listing at source)