Senior Full Stack Engineer

Zipher is building the Autonomous Execution Layer for cloud data and AI workloads. Backed by $50M in funding, we dynamically orchestrate clusters, predict bottlenecks, and auto-heal infrastructure in real time — with zero human intervention. Our platform runs in production at global enterprise customers, including Fortune 500 companies, delivering mission-critical resilience and sub-second optimization.

Autonomous execution is a new category, and we are the ones defining it — already running in production inside Fortune 500 environments, where being wrong is not an option, and already selling in millions of dollars of enterprise contracts. The team is deliberately talent-dense; every hire changes what we are able to build next.

We are looking for a Senior Full Stack Engineer to own Zipher’s entire product surface — the control plane that turns autonomous decisions into something engineering leaders can see, trust, and act on. You will own it end to end: the services and APIs behind it, and the real-time interfaces built on top of them. This is a backend-heavy role. It is best suited for candidates whose primary expertise and passion lie in backend development

What You’ll Do

• Own the product surface end to end: architecture, services, APIs, and the real-time interfaces built on top of them
• Set the technical direction of the control plane — stack, data contracts, and the patterns every engineer after you builds on
• Build high-throughput services that turn live telemetry from production data and AI workloads into decisions, and the explainability layer that makes those decisions readable
• Work directly with the founders on what gets built next, and lead the technical moves that follow — from what enterprise customers need to the architecture that answers it
• Drive quality end to end: testing, CI/CD, observability, and the operational bar for a product Fortune 500 engineering teams run in production
  
  

• A product that is already winning: a new category of autonomous data platform, in production inside Fortune 500 environments, with millions of dollars in enterprise contracts already signed
• A domain of your own: you lead it end to end — the architecture, the stack, and the patterns every engineer after you builds on
• A seat on the core team: you are one of the engineers deciding what the platform becomes, not one implementing someone else’s decisions
• Engineering problems that are genuinely hard: real-time telemetry at enterprise scale, distributed decisions, and an explainability layer teams trust in production
• Engineers worth learning from: a deliberately talent-dense team, direct work with the founders, and a bar that pulls everyone up
• Room to grow and to lead: your ownership widens as the platform does, and the people who lead moves here keep leading them
• Top-of-market compensation and a meaningful equity package
  
  

• 7+ years of full stack engineering experience, with most of it on the backend — production services, APIs, and data-intensive systems
• Strong production mastery of Python (FastAPI) and TypeScript/React, plus 2+ years of hands-on production experience with AWS — Lambda, ECS, Fargate, DynamoDB, ECR, S3, API Gateway, or EventBridge
• Experience from leading startup engineering teams, building cyber, AI, or data-intensive products where scale, latency, and correctness are the hard part
• A track record of taking systems from architecture to production at scale, and owning them once they are there
• BSc in Computer Science, Electrical Engineering, or a related engineering discipline
• A high-agency, maker mindset: strong product intuition, engineering craftsmanship, and full ownership of what you ship
  
  

• Experience as a developer in an elite IDF technology unit (e.g. 8200, Mamram, Ofek, Matzpen) or another high-performance engineering environment
• Experience designing real-time, event-driven, or high-throughput cloud architectures
• Depth in modern frontend: TypeScript, state management, and component architectures for data-dense interfaces
• Background in observability and telemetry products, developer-facing platforms, or large-scale data platforms such as Spark, Databricks, or Snowflake