Research Scientist

Engram Lab•San Francisco, CA

9d•Onsite

About The Position

You will join a small, focused team of researchers and engineers working at the frontier of learning and memory. As a Research Scientist, you’ll design experiments, develop new recipes, build evals, and shape the product used by some of the world's leading tech and AI companies. Specifically, this includes: Memory and knowledge internalization — designing and evaluating methods for encoding large, heterogeneous document corpora into compact parametric memory (e.g., LoRA/adapter-based representations, prefix tuning, state-space methods). Synthetic data and self-study — understanding what makes synthetic training data generalize, and developing self-study pipelines that allow models to reflect on and consolidate new context. Continual learning algorithms — tackling catastrophic forgetting, sequential updates, knowledge conflicts, and the tradeoffs between in-weights memory and agentic retrieval. RL and online training — exploring reinforcement learning methods that let models improve from interaction and feedback in real deployment settings. Scaling and capacity — empirically studying how model capacity, data scale, and compute interact; developing the scaling laws that inform our product roadmap. Our team is passionate about the problems we are solving. We work up and down the stack, and the line between research and engineering is blurry by design. We're pragmatic and problem-driven, collaborative to our core, and hold a high bar for everything we ship.

Requirements

A deep background in machine learning, with strong fundamentals in inference serving systems, KV cache design, or latency-sensitive model deployment.
A track record of rigorous ML research — publications, strong open-source contributions, or equivalent demonstrated depth.
Extensive experience in at least one area directly relevant to our work: continual learning, memory architectures, test-time training (TTT), parameter-efficient finetuning, context compression, retrieval, synthetic data, distillation, or agents.
Comfort working up and down the stack — you understand both the research question and the system it runs on — not just describe an idea in a paper and hand it off.
Strong technical communication: you can explain complex ideas simply and engage in high-bandwidth, generative technical conversation.

Nice To Haves

Experience bridging research and product — shipping things that real users interact with.
Familiarity with LLM training infrastructure.

Responsibilities

Design experiments
Develop new recipes
Build evals
Shape the product
Design and evaluate methods for encoding large, heterogeneous document corpora into compact parametric memory (e.g., LoRA/adapter-based representations, prefix tuning, state-space methods)
Develop self-study pipelines that allow models to reflect on and consolidate new context
Tackle catastrophic forgetting, sequential updates, knowledge conflicts, and the tradeoffs between in-weights memory and agentic retrieval
Explore reinforcement learning methods that let models improve from interaction and feedback in real deployment settings
Empirically study how model capacity, data scale, and compute interact
Develop scaling laws that inform our product roadmap