Software Engineering Manager - Search

Leadership & Team Building

• Build the Team: Recruit, hire, and mentor a high-performing group of backend and ML engineers covering search infrastructure, computer vision, and applied AI.
• Strategic Oversight: Own the end-to-end roadmap for Search and ML engineering backend from product-facing features like POI, LPR, and AI Search to the platform services that power them.
• Cross-Functional Partnership: Partner closely with Product, Design, CV/ML research, Camera Firmware, and Infrastructure teams to align on priorities, dependencies, and deployment plans.

AI Search & Applied ML (Hands-On)

• Agentic & Generative AI: Drive the rollout of AI-Powered Search, LLM migrations, VLM experimentation, and agentic AI into production-grade features.
• Embeddings & Retrieval: Oversee the evolution of our vector search stack to improve recall, latency, and cost at fleet scale.
• Model Evaluation: Lead detection evaluation, model consistency, and ongoing quality improvements for various CV and ML pipelines.

Search Infrastructure & Platform Service Ownership

• Accountable for a portfolio of production services including submission endpoints, inference pipelines, APIs and database layer.
• Migrations & Deprecations: Execute in-flight migrations into new advanced pipelines and deprecations of old pipelines without disrupting customer-facing features.
• Scalability & Performance: Drive large-org optimizations, inference engine stability, gRPC load balancing, and connection-hardening work to keep the pipeline healthy as the fleet grows.

Reliability & Quality

• On-Call & Incident Response: Own the team on-call rotation, post-mortem quality, and the new programs required to scale the team.
• Test Coverage: Expand integration test coverage and search pipeline change testing to catch regressions before they reach production.
• Telemetry & Dashboards: Define and track the metrics that matter (e.g. API latency, search quality, inference stability, field reliability, etc.) via dashboards and service SLOs.

What You Bring

• 7+ years of software engineering experience, including 2+ years managing backend or ML engineering teams.
• Track record leading 5+ engineers running production services at meaningful scale.
• 4+ years of hands-on experience in at least two of: information retrieval, vector / embeddings-based search, computer vision, or large-scale recommendation systems.
• 2+ years productionizing modern LLMs, VLMs, or agentic systems (evals, guardrails, latency/cost tuning).
• Deep proficiency in Python plus Go/Java/C++; fluent in distributed systems (gRPC, Kafka) and a major cloud provider (AWS preferred).
• Hands-on with at least one production vector database or search engine (OpenSearch, Turbopuffer, FAISS, Milvus, pgvector, etc.).
• Strong operational instincts: on-call ownership, post-mortem rigor, and a habit of defining metrics and building evals to drive quality on high-availability services.