About the role#
The Agents team focuses on building, aligning, and scaling frontier AI systems capable of executing complex, multi-step tasks across text and speech. This role sits at the intersection of agent capabilities, human-computer interaction, and infrastructure. You will work on challenges related to alignment, reliability, and scalability, with potential projects involving post-training methods for agentic behavior, evaluation frameworks for open-ended tasks, and infrastructure for agent operations at scale.
What you'll do#
- Research and implement novel techniques in focus areas such as agentic workflows and scientific domains.
- Design and conduct rigorous experiments to validate hypotheses.
- Document findings in scientific publications and blog posts.
- Communicate project plans, progress, and results to the broader team.
- Contribute to research directions including RL recipes for self-learning, long-context task completion, and safety paradigms for agentic behavior.
What you'll need#
- Currently pursuing a Masters or Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field.
- Publications at leading ML, NLP, or speech conferences or journals.
- Strong knowledge of Machine Learning and Deep Learning fundamentals.
- Experience with deep learning frameworks such as PyTorch or JAX.
- Strong programming skills in Python.
- Understanding of LLMs, Transformer architectures, and recent developments in foundation models.
Location & details#
- Location: San Francisco, California
- Term: Fall 2026 (September 14th to December 18th)
- Duration: 12 to 16 weeks
- Modality: On-site
- Employment: Full-time
- Compensation: Paid ($58 - $63 per hour)
