Applied Research Scientist, Agents

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. 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.

Responsibilities

• 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.

Requirements

• 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 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.
• Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).

Qualifications

• Experience with natural language processing (NLP) and large language models (LLM).
• Knowledge of human-computer interaction (HCI) principles and techniques.
• Understanding of ethical considerations in AI and machine learning.

Skills

• Python programming.
• Data analysis and visualization.
• Machine learning algorithms and models.
• Reinforcement learning.
• Research methodology and publication.

Benefits

At Labelbox, we strive to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is $250,000 - $300,000 USD. We offer a competitive benefits package that includes:

• Health insurance.
• Retirement savings plan.
• Flexible work schedule.
• Professional development opportunities.

Pay

The annual base salary range for United States-based candidates is $250,000 - $300,000 USD.

Schedule

We offer a hybrid work style with 3 days per week in the office, combining collaboration and flexibility.

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.

Life at Labelbox

• Location: Join our dedicated tech hub in San Francisco.
• Work Style: Hybrid model with 3 days per week in office, combining collaboration and flexibility.
• Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making.
• Growth: Career advancement opportunities directly tied to your impact.
• Vision: Be part of building the foundation for humanity's most transformative technology.

Our Vision

We believe data will remain crucial in achieving artificial general intelligence. 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.

Contact Information

If you have any questions about this position, please contact [Contact Email]. For job alerts and other opportunities, visit our careers page.