Shape the Future of AI
\nAt 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.
\nAs 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.
\nCreate frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.
\nDesign agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.
\nDevelop data pipelines from diverse sources like code repositories, web browsers, and computer systems.
\nImplement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.
\nEngage 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.
\nCollaborate closely with frontier AI lab customers to understand requirements and guide model development.
\nPublish research findings in academic journals, conferences, and blog posts.
\nWhat You Bring
\nPh.D. or Master's degree in Computer Science, Machine Learning, AI, or related field.
\nAt least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.
\nExperience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.
\nConstructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).
\nAdept at interpreting research literature and quickly turning new ideas into prototypes.
\nDeep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.
\nProficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
\nStrong analytical and problem-solving abilities in ambiguous situations.
\nExcellent communication skills.
\nTrack record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).
\nLabelbox Applied Research
\nAt Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced 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.
\nLife at Labelbox
\nLocation: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
\nWork Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
\nEnvironment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
\nGrowth: Career advancement opportunities directly tied to your impact
\nVision: Be part of building the foundation for humanity's most transformative technology
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