San Francisco Bay Area
We're hiring a Recommendation Engineer to build the system layer that connects Nirva's memory, context, and user understanding to every content generation feature we ship.
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.
Build the recommendation and personalization layer between personal context or memory and all content generation features, including journaling, daily recaps, emotional insights, proactive experiences, coaching suggestions, conversation starters, and chat.
Ranking and decisioning across relevance, recency, emotional salience, novelty, diversity, usefulness, timing, frequency, and when to say nothing at all.
A unified signals and context service so product surfaces consume ranked, explained personal context through one layer instead of each rebuilding retrieval and personalization.
Feedback loops that turn implicit and explicit signals, dismissals, edits, and engagement into ranking improvements and data flywheels.
Cold start and learning over time so Nirva is useful on day one and measurably more personal by week four.
Offline and online evals for generated content including relevance, specificity, repetition, timing, and trust, with A/B testing and not just click-through.
2+ years of full-time engineering experience, or a PhD in computer science, machine learning, information retrieval, recommender systems, or a related field, plus 2 applied internships where you shipped or evaluated real systems.
Hands-on experience building recommendation, ranking, search, or personalization systems used by real users, with evidence of impact on quality or engagement.
Strong knowledge of retrieval, embeddings, ranking, and context engineering for LLMs, and how to connect a ranking layer to content generation.
Experience with feedback loops and experimentation, including logging the right signals, offline evals, A/B tests, and separating clicks from real usefulness and personalization.
Location: San Francisco Bay Area, in person.
Type: Full-time.
Pay: $180k–$350k, level dependent, plus meaningful equity.