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.
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
Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.). Labelbox Applied Research
Requirements
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:
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
Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
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
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.
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.
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).
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.
We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations.
Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.
As AI models become more sophisticated, the need for high-quality, specialized training data will only grow.
Join us in developing new products and services that enable the next generation of AI breakthroughs.
Our customers include Fortune 500 enterprises and leading AI labs.
Benefits
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below.
This range is not inclusive of any potential equity packages or additional benefits.
Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range $250,000 — $300,000 USD Life at Labelbox
Vision : Be part of building the foundation for humanity's most transformative technology Our Vision
Contact
Any emails from Labelbox team members will originate from a @labelbox.com email address.
Additional details
Enterprise Platform & Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
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.
Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
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
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.
Strong analytical and problem-solving abilities in ambiguous situations.
We foster an environment of intellectual curiosity, collaboration, and innovation.
Location : Join our dedicated tech hub in San Francisco