Research Engineer​/Scientist, Post-Training

Overview

Solving Self-Improving Superintelligence. The human brain is a sponge. Today’s AI brains are brittle and rigid. At Letta, we’re building self‑improving artificial intelligence: creating agents that continually learn from experience and adapt over time. Founded by the creators of MemGPT from UC Berkeley’s Sky Computing Lab (the birthplace of Spark and Ray). Backed by Jeff Dean, Clem Delangue, and pioneers across AI infrastructure.

Our agents already power production systems at companies like 11x and Bilt Rewards, learning and improving every day. We’re assembling a world‑class team of researchers and engineers to solve AI’s hardest problem: making machines that can reason, remember, and learn the way humans do.

Note that this role is in‑person (no hybrid), 5 days a week in downtown San Francisco.

You will pioneer post‑training techniques that improve how well LLMs can be integrated into complete agentic systems. At Letta, you’ll work with a world‑class, tight‑knit team of AI researchers and engineers towards our vision of self‑improving superintelligence. Advance the field through open publishing of research through papers, technical reports, blog posts, and open‑source code.

• Training models for better agentic tool‑use, particularly for context management.
• Designing mechanisms for continuous model weight updates post‑deployment without catastrophic forgetting.
• Designing and running experiments to improve understanding of the interplay between data mixtures, training algorithms, and models.
• Building infrastructure for generating and collecting synthetic data at scale.
• Building challenging evals for measuring agentic capabilities.

• Proficiency in Python and deep learning frameworks (e.g. PyTorch).
• Expertise in post‑training techniques (e.g. SFT fine‑tuning, reinforcement learning, reward models, preference learning).
• Ability to balance execution speed with empirical rigor.
• Proven track record of impactful research (breakthrough publications and/or open‑source contributions).
• Real‑world impact beyond pure academic work.