Sr Software Engineer, AI Experience

NRG•VIRTUAL, WA

3d•$153,000 - $185,000

About The Position

We are seeking a Sr AI Experience Engineer to build personalization and recommendation systems that make Vivint AI experiences more relevant to each household. This role will be responsible to: Design and build personalization, recommendation, ranking, retrieval, candidate-generation, and re-ranking systems. Develop household, user, device, event, and context representations using behavioral signals, embeddings, feedback, and product telemetry. Build feedback loops from searches, dismissals, saves, notification engagement, customer actions, and product usage. Partner with Agentic AI and AI Platform teams to connect short-term context, long-term memory, retrieval, and personalization. Design experiments with Product and Analytics, including A/B tests, holdouts, interleaving experiments, contextual bandits, multi-armed bandits, or causal measurement where appropriate. Deploy and monitor production recommendation systems with attention to latency, reliability, cost, privacy, and customer trust.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 5+ years of professional experience in software development, applied science, or ML engineering; or Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 2+ years of professional experience in software development, applied science, or ML engineering
Experience with ranking, retrieval, recommendation, personalization, or learning-to-rank systems
Strong Python, SQL, data engineering, and production ML skills
Experience with embeddings, behavioral datasets, online/offline evaluation, and experimentation
Experience deploying ML systems into production with CI/CD and monitoring
Strong communication skills and ability to partner with product, analytics, design, and engineering teams

Nice To Haves

Experience with collaborative filtering, content-based recommendations, two-tower models, sequence models, or deep learning recommenders
Experience with feature stores, vector retrieval, model serving, contextual bandits, or reinforcement learning
Experience optimizing engagement, retention, click stream, notification relevance, customer satisfaction, or conversion metrics
Experience with GenAI recommenders, RAG, AI memory, or agentic personalization
Experience with smart home, IoT, security, energy, mobile, or device telemetry data

Responsibilities

Design and build personalization, recommendation, ranking, retrieval, candidate-generation, and re-ranking systems.
Develop household, user, device, event, and context representations using behavioral signals, embeddings, feedback, and product telemetry.
Build feedback loops from searches, dismissals, saves, notification engagement, customer actions, and product usage.
Partner with Agentic AI and AI Platform teams to connect short-term context, long-term memory, retrieval, and personalization.
Design experiments with Product and Analytics, including A/B tests, holdouts, interleaving experiments, contextual bandits, multi-armed bandits, or causal measurement where appropriate.
Deploy and monitor production recommendation systems with attention to latency, reliability, cost, privacy, and customer trust.