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

What We Offer

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

Ready to own the control plane for an autonomous data platform? Hit Apply.

Requirements:

What You’ll Bring

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

Nice to Have

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