New York, NY, HybridYesterday
Sei Labs
About Sei Labs: Sei Labs builds open sourced technology for the high-performance Sei Blockchain, the first parallelized EVM Layer 1 blockchain designed to scale with the industry. The unique optimizations built into Sei enable Web3 applications to reach Web2 level scale and performance, driving the mass adoption of digital assets. Our teams are comprised of former Google, Goldman Sachs, Robinhood, Nvidia, and Databricks veterans dedicated to onboarding the next billion users onto a vibrant ecosystem of applications. Sei Labs has raised over $30M from top investors including Jump Crypto, Multicoin Capital, Coinbase Ventures, Distributed Global, Hudson River Trading, and Flow Traders. Platform Engineering at Sei: We're building Sei into a world-class L1 for decentralized finance. This role exists to make the safe path the default path for every engineer on the team — observable, tested, reversible, and fast. You'll be a foundational hire on a small Platform Engineering team with a broad mandate. What you'll own: Infrastructure & scale testing. Build and maintain Docker/Kubernetes harnesses for repeatable stress and scale testing, used by every core team as a pre-release gate. Run our multi-cloud footprint with explicit reliability and cost targets. Release engineering & CI/CD. Consolidate CI/CD into reusable, standardized workflows. Own version tagging, hotfix paths, PR review scaffolding, and the full release lifecycle. Observability. Single pane of glass for metrics, traces, and logs — correlated by block height, powered by OpenTelemetry. HA Prometheus, ELK, defined SLOs on every critical service. Performance & chaos testing in CI. Automated chaos and performance tests gating 100% of releases. Regressions caught before merge, not by users. Security & incident response. Run the bug and vulnerability workflow end to end — auditors, Immunefi, triage, postmortems. Every incident produces a new test, metric, or runbook.
5+ years building production systems at scale, designed and shipped infrastructure, Security fundamentals, Threat modeling, common vulnerability classes, security work integration into development pipeline, Distributed consensus understanding, algorithmic level consensus protocols, safety vs. liveness, fault assumptions, failure modes, debugging at consensus layer, Benchmarking, performance bottleneck analysis, designing benchmarks, profiling under realistic load, finding bottlenecks, Live incident response, operated systems with real consequences, triaging under pressure, clear communication, turning postmortems into structural fixes, End-to-end systems thinking, built systems, opinions on monitoring stacks, CI/CD, runtime, chaos testing, Blockchain or web3 infrastructure experience, consensus, mempool, storage layers, Multi-cloud cost programs, AI-assisted security tooling integration