Labelbox

Labelbox

Applied Research Scientist, Agents

San Francisco Bay Area

Sponsorship not specified$250k-$300kDetected 43 days ago
PythonMachine LearningDeep LearningTensorFlowPyTorchData EngineeringData ScienceLLMsControlsResearchCommunicationCollaborationProblem SolvingAdaptability

About the role

  • 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.

Responsibilities

  • Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.
  • 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.
  • We celebrate those who take ownership, move fast, and deliver impact.
  • You'll publish meaningful results, collaborate with customer researchers in frontier AI labs, and turn prototypes into reliable, scalable features.
  • Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact.
  • We empower people to drive results through clear ownership and metrics.
  • Collaborate closely with frontier AI lab customers to understand requirements and guide model development.

Requirements

  • Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).
  • At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.
  • Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or related field.
  • 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 human data requirements.
  • Proficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
  • Strong analytical and problem-solving abilities in ambiguous situations.
  • Excellent communication skills.
  • Labelbox Applied Research
  • At 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.

Skills

  • Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale

Compensation

  • Labelbox strives to ensure pay parity across the organization and discuss compensation transparently.

Benefits

  • Continuous Growth: Every role requires continuous learning and evolution.
  • Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.

Company info

  • 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
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This listing is sourced directly from Labelbox's careers page and normalized into a canonical job model.