Research Scientist - Agents
$150k - $450k • Sunnyvale, CA • Full-time
Posted 9mo ago
Job Location
Sunnyvale, CA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Agent Engineer
About the job
The Institute of Foundation Models is a research lab focused on building, understanding, using, and risk-managing foundation models. We aim to advance research, nurture AI builders, and drive contributions to a knowledge-driven economy. As part of our team, you will work on cutting-edge foundation model training alongside world-class researchers and engineers, tackling fundamental challenges in AI development and contributing to groundbreaking AI solutions.
As a member of the Agents team, you will address research and engineering challenges in training advanced agentic language models capable of reasoning and tool use for computer-based tasks. This involves developing high-quality training datasets, robust evaluations, and innovative reinforcement learning infrastructure, algorithms, and environments. We seek candidates with expertise across the research-engineering spectrum, from algorithmic research to large-scale systems and optimization.
Responsibilities
- Propose, prototype, and scale algorithms and systems for agentic learning.
- Develop new paradigms for agentic behavior.
- Contribute to technical reports, research publications, and open-source software.
- Collaborate with other teams to produce state-of-the-art foundation models.
Requirements
- BS, MS, or PhD degree (or equivalent experience) in Computer Science, Machine Learning, or related fields.
- Minimum 2 years of experience in LLM training/fine-tuning, evaluations, reinforcement learning, LLM inference, or distributed machine learning systems.
- Python and PyTorch development experience.
- Experience using LLM agents for personal or professional tasks.
- Experience designing and implementing algorithms from scratch.
- Experience in dataset curation and generation.
- Experience building or contributing to training/serving infrastructure.
- Experience designing and analyzing evaluations.