Engineering Manager, Decision Systems

San Francisco, CA /
Engineering /
We are looking for an engineering manager to lead the Decision Systems team. Decision Systems ingest real-time streams of sensor data and ML inferences from Zippin powered stores, and provide higher level services such as accurate Carts and Inventory Tracking. In this role, you will report to VP of Engineering, and provide technical leadership as well as manage engineers.

Zippin provides autonomous checkout for leading brick & mortar retailers. Shoppers just walk into stores, pick up the items they want, and then leave. We use machine learning, computer vision, and sensor fusion to automatically charge shoppers when they walk out—no more checkout lines.

Zippin is headquartered in San Francisco, CA, with additional offices in San Carlos - CA, Toronto - Canada, and India. Industry veterans from Amazon, SRI, and VMware founded the company and raised over $15M in venture funding from Evolv, NTT Docomo, SAP, and Maven Ventures.

This position can be remote (in US) or located in San Carlos, CA or San Francisco, CA.


    • Own architecture, roadmap, and execution for short and long term deliverables
    • Provide technical guidance to the team
    • Drive initiatives/projects in collaboration with other teams
    • Instill a spirit of continuous improvement in the team’s code, architecture, and processes
    • Hire, coach, and grow top talent


    • Strong fundamentals and experience in large-scale distributed systems design and development
    • Excel at planning, and overseeing execution to meet commitments and deliver with predictability
    • Ability to hire while ensuring a high hiring bar, keep engineers motivated, mentor and handle performance management
    • Good problem solving and communication skills
    • 10+ years of software development experience, with at least 3 years managing engineering teams
    • Programming experience in one of the following: C++, Python, or Java

Bonus Points

    • Managing teams in a dynamic (start up like) environment
    • Experience running production services in public clouds
    • Machine Learning experience
    • Knowledge of Kubernetes and Docker