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.

We are looking for a Founding Backend Engineer to build the systems behind Zipher’s autonomous execution engine. You will own core infrastructure that turns high-volume telemetry and changing cloud conditions into reliable, real-time decisions across production data and AI workloads.

What You’ll Do

• Architect and scale the core backend services that power real-time workload orchestration, optimization, and self-healing
• Build resilient, high-throughput systems for processing distributed state, event streams, and production telemetry at scale
• Design infrastructure that makes autonomous decisions observable, explainable, and safe for enterprise engineering teams
• Partner closely with data, ML, and platform engineers to bring optimization models and control loops into reliable production systems
• Drive architectural decisions, raise the engineering bar, and own services from design through deployment, observability, and incident response

What We Offer

• A chance to build the core execution engine for a new category of autonomous cloud infrastructure
• High ownership from day one: real architectural influence, direct exposure to founders, and responsibility for mission-critical systems
• Technical, high-velocity team that values curiosity, speed, and engineering craftsmanship
• Top-of-market compensation and meaningful equity

Ready to build the infrastructure that lets data and AI workloads run autonomously?

Hit Apply.

What You’ll Bring

• 6+ years of backend engineering experience, including ownership of production-grade distributed systems, infrastructure platforms, or core product architecture
• Strong production experience with Python or Go, and a track record of designing robust APIs, services, and asynchronous workflows
• Deep hands-on experience operating cloud-native systems on AWS at scale, including EMR, DynamoDB, Kinesis, Lambda, S3, or API Gateway
• Experience with distributed computing or infrastructure engines, such as Kubernetes, Spark, high-throughput schedulers, streaming platforms, or message brokers
• A high-agency, engineering-first mindset: you enjoy ambiguous, high-leverage problems and take responsibility for reliability, performance, and quality

Nice to Have

• Experience with large-scale compute and data platforms, including Spark, Databricks, Trino, Flink, or Snowflake
• Experience building infrastructure for AI/ML workloads, MLOps/AIOps systems, or production model-serving environments
• Familiarity with cloud cost optimization, FinOps, workload scheduling, or efficiency optimization at scale
• Experience in an elite IDF technology unit (e.g. 8200, Mamram, Matzpen) or another high-performance engineering environment