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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 Senior 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 data platform
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.
Requirements
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