Research Scientist
Engram· Member of Technical Staff
San FranciscoFull-timePosted 4mo ago
About the role
Research Scientist role at Engram focused on learning and memory at the frontier of ML, building experiments, evals, and product-facing research across memory, synthetic data, continual learning, RL/online training, and scaling.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Required:Machine Learning FundamentalsResearch Publications / Open-SourceContinual LearningMemory Architectures / RetrievalSystem-Research Bridging
Benefits
- Competitive cash compensation and startup equity.
Last updated Aug 4, 2026. Interested? Apply on the company site.
About Engram
AI has learned the world. Now it should learn yours.
Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training – and they’re trained mostly on the public internet.
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