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Own product surfaces end to end, spanning backend systems, APIs, user experience, deployment, observability, evaluations, and feedback loops. Work directly with AI companies and users to design products using models with memory. Prototype agentic products and internal tools, train models for demonstrations, develop telemetry and evaluations, collaborate with model researchers, and help establish product engineering standards.

Engram builds models that create compact, personal "engrams" (memories) to make AI context-aware and efficient. This posting invites interested candidates to submit an expression of interest for future roles or opportunities.

Design and evaluate methods for compact parametric memory, synthetic data and self-study pipelines, continual learning, RL/online training, and empirical scaling studies. Build experiments, evals, and production-ready research that spans research and systems engineering.

Design, optimize, and scale training and inference workloads for personalization and continual learning. Productionize research prototypes, improve latency/reliability/efficiency, optimize per-user serving and memory retrieval, and work on distributed training across GPUs and nodes. Collaborate with researchers, platform engineers, and customers; establish engineering practices, testing, on-call, and security posture.

Design and implement data infrastructure for personalization and continual learning APIs: secure storage for user context and per-user model weights, ingestion and preprocessing pipelines for continual training, data observability and quality controls, reliable integrations with customer APIs, and establish engineering standards for security, testing, and on-call while helping grow the engineering team.