Platform Engineer

We’re a fast-moving AI/ML platform startup building infrastructure for reinforcement learning environments, post-training data pipelines, and large-scale agent evaluation. Our engineering team of ~15 includes exceptional technical talent - competition medalists, serial AI startup founders, and published researchers., * Own production uptime, latency, provisioning speed, infrastructure cost, and incident response for core platform services.

• Build and maintain AWS infrastructure using Terraform, Kubernetes/EKS, Helm, Docker, EC2, CodeBuild, ECR, S3, IAM, networking, and secrets management.
• Design and improve backend and platform systems for scale - including capacity planning, autoscaling, queueing, backpressure, cleanup jobs, retries, and rollback paths.
• Define and improve dashboards, alerts, logs, traces, SLOs, runbooks, and on-call workflows so failures are detected, debugged, and resolved quickly.
• Build reliable CI/CD pipelines, release automation, environment management, and deployment workflows that improve developer productivity and reduce production risk.
• Write clean, maintainable code to automate systems, improve backend services, and create internal tooling.

• 2-4 years of experience owning production cloud infrastructure for a high-availability, user-facing platform, with responsibility for uptime, performance, deployment safety, and cost.
• Deep hands-on experience with AWS and containerized systems; strong familiarity with Terraform, Kubernetes/EKS, Docker, EC2, CodeBuild, ECR, S3, IAM, networking, and secrets management.
• Proven track record building or operating CI/CD, release automation, observability, alerting, and incident response systems.
• Strong backend engineering judgment - ability to reason about service architecture, APIs, databases, async systems, queues, scaling limits, and production failure modes.
• Ability to write clean, maintainable code and apply software engineering judgment across infrastructure, backend systems, and developer workflows.
• High ownership mindset; comfortable being accountable for production systems end-to-end.

Nice to Have

• Experience designing systems for bursty workloads, long-running jobs, sandboxed execution, distributed workers, or high-concurrency services.
• Background operating infrastructure for data-heavy, ML/AI, workflow, marketplace, developer-tools, or enterprise platforms.
• Demonstrated focus on reducing cloud spend through better architecture, autoscaling, workload placement, caching, cleanup systems, or observability.

• Salary: $150,000 - $250,000 USD annually (full-time, US-based).
• Equity participation in an early-stage, well-funded AI startup.
• Work alongside a world-class technical team on infrastructure that operates at real scale.
• High degree of autonomy and direct impact on product and platform direction.