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Design experiments and develop methods for encoding large document corpora into compact parametric memory. Develop self-study pipelines, continual learning algorithms, and RL methods to improve model performance from interaction.
Requires a deep background in machine learning with a track record of rigorous research through publications or open-source contributions. Must have extensive experience in memory architectures, PEFT, or retrieval and be comfortable working across the full research-to-engineering stack.
Today’s AI is a brilliant stranger: it can solve the world’s hardest math problems, but it knows next to nothing about you and your work. It rereads your files to answer even basic questions, burns an enormous amount of tokens when sifting through large corpuses, and between sessions, it retains scraps at best.
We train models to study your world and anticipate your questions in advance, forming engrams: compact memories that capture your knowledge and history. Our approach opens a new axis of scaling. The more we study your context at training time, the better we become at inference time.
We're already working with leaders in AI like Microsoft, Notion, and Harvey, and just raised $98M from General Catalyst, Kleiner Perkins, Sequoia, Factory, Modern, Amplify, Neo and others. Our investors and advisors include Assaf Rappaport, Andrej Karpathy, and Pieter Abbeel.
AI has spent years learning everything about the world. Now it should learn something about yours.
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.
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.
Experience bridging research and product — shipping things that real users interact with.
Familiarity with LLM training infrastructure.
Engram is based in San Francisco. This role is in-person in our SF office. We offer competitive cash compensation and startup equity.
Engram is an equal opportunity employer. We’re building a team that reflects a range of backgrounds and perspectives, and we welcome applicants regardless of race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, or veteran status.
ENGRAM Lab è la divisione del gruppo ENGRAM dedicato alla formazione dei 3D artist e al loro inserimento professionale.
La visualizzazione architettonica è uno strumento sempre più indispensabile nella comunicazione di un progetto di architettura e quella del 3D artist è ormai una professione a tutti gli effetti. Come tale, questa richiede delle competenze specifiche, totalmente diverse rispetto a quelle di un progettista architettonico. Ciò non significa che chi lavora nel settore sia semplicemente un tecnico specializzato nell'uso dei software di rendering e modellazione 3D. Padroneggiare gli strumenti tecnologici rimane indispensabile, ma un buon 3D artist deve anche conoscere i principi sui quali si basa la comunicazione tramite immagini, e sviluppare un workflow efficiente che risponda alle esigenze di ciascun progetto in modo flessibile e rapido. Questa idea è alla base della filosofia di ENGRAM Lab. Prima ancora di essere una scuola, ENGRAM Lab è un incubatore di talenti. Il nostro obiettivo è formare professionisti della visualizzazione architettonica non soltanto negli aspetti tecnici ma soprattutto in quelli concettuali e operativi necessari a sviluppare immagini per l'architettura in contesti lavorativi reali. Il risultato è un'offerta didattica unica, sintesi di oltre due decenni di esperienza di ENGRAM Studio nella produzione di immagini e filmati 3D per i grandi nomi dell'architettura contemporanea.
Offices: Via San Giovanni Bosco, 1, Faenza, Emilia-Romagna 48018, IT
View all jobs at ENGRAM LabBased on 3643 listings with disclosed salaries, most software jobs in San Francisco, CA pay between $140k–$289k per year. Individual offers vary with seniority, company size, and specialization.
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