Sr Software Engineer, Video Intelligence

NRG•VIRTUAL, WA

1d•$150,000 - $180,000

About The Position

Vivint pioneers cutting-edge smart home security solutions, empowering customers to live a safer, smarter, and more sustainable life. We seek passionate, innovative engineers eager to transform the daily lives of millions through groundbreaking solutions. We blend intelligent hardware, real-time sensor data, and AI to transform how people interact with their homes. We are seeking a Sr Video Intelligence Engineer to advance video understanding. This role will build video-language, summarization, context, and event-reasoning capabilities that improve camera intelligence 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 LLMs, vision-language models, multimodal AI, or generative AI systems
Strong Python software engineering skills
Experience processing, analyzing, or serving video content at scale
Familiarity with event detection, summarization, semantic understanding, embeddings, or model evaluation
Experience deploying AI services into production with Git and CI/CD workflows

Nice To Haves

Experience generating natural-language descriptions, summaries, or searchable representations from video
Experience with multimodal foundation models, temporal video understanding, re-identification, or vector retrieval
Experience improving precision/recall, reducing false alerts, or evaluating customer-facing camera AI
Experience with smart home, security camera, IoT, edge/cloud, or real-time inference architectures
Experience with Docker, Kubernetes, GCP/AWS, or production model monitoring

Responsibilities

Own VLM strategy, video-language understanding, temporal video reasoning, event understanding, and multimodal embeddings.
Improve AI Video Search, video summarization, alert relevance, recognition support, and false-alert reduction.
Build and evaluate systems that generate useful natural-language descriptions, insights, and summaries from video.
Partner with AI, platform, product, and app teams to move models from research to production.
Develop production evaluation methods for precision, recall, grounding, latency, safety, and customer usefulness.
Guide architecture decisions across cloud, edge, and hybrid inference paths for video intelligence.