San Francisco Bay Area
We're hiring an AI Memory Engineer to build how Nirva remembers a person over time: the memory system at the heart of the personal companion.
Nirva is building the first AI fashion accessory for your mind — a necklace or bracelet that privately listens only to the wearer to enable proactive journaling, emotion tracking, and life coaching. Think Oura Ring for your Mind.
Backed by Lightspeed and South Park Commons, recognized at CES within 90 days of founding, and launching in the U.S. this fall. Co-founders bring backgrounds from Meta Reality Labs (Quest, Ray-Ban glasses) and fashion industry.
Turn messy transcripts, events, and moments into memory that is accurate, updatable, retrievable at the right moment, and controllable by the user.
Build the long-term memory system, including episodic memory, semantic memory, and evolving user models.
Build extraction pipelines that turn transcripts, context, events, and interactions into structured memories with source, confidence, and time attached.
Own consolidation, summarization, and hierarchy from raw moments into daily summaries, life chapters, people profiles, and durable preferences.
Own retrieval and context injection so the right memories reach prompts and agent workflows within a context budget.
Handle updating, correction, conflict resolution, and forgetting so old information can be replaced, contradictions resolved, stale memories decayed or archived, and hallucinated memories never become fact.
Build for user trust and control so users can see, edit, and delete what Nirva remembers.
Run memory evals including recall and precision, stale and hallucinated memory rates, and longitudinal consistency.
2+ years of full-time engineering experience, or a PhD in computer science, machine learning, NLP, information retrieval, or a related field, plus 2 applied internships where you shipped or evaluated real systems.
Hands-on experience building a memory system, personal knowledge system, or long-term personalization system, not just a one-shot RAG demo.
Strong working knowledge of LLMs, embeddings, retrieval, structured outputs, summarization, classification, and context engineering.
Experience structuring personal data through knowledge graphs, entity and relationship models, timelines, or context stores.
Strong eval and debugging mindset, and care for accuracy, provenance, confidence, privacy, and user control.
Location: San Francisco Bay Area, in person.
Type: Full-time.
Pay: $180k–$350k, level dependent, plus meaningful equity.