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You will own the reliability, scale, and performance of core infrastructure for an AI/ML platform while managing production uptime and incident response. Additionally, you will build and maintain AWS infrastructure and CI/CD pipelines to improve developer productivity and system efficiency.
This is a backend-architecture-heavy platform engineering role sitting within a tight-knit engineering team of roughly 15 people. You will own the reliability, scale, performance, and developer experience of core infrastructure and systems for an AI/ML evaluation and reinforcement learning platform. The work has direct, measurable impact on how fast, reliable, and cost-effective the platform is to build on and operate.
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, covering 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 developer tooling.
2 to 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; Terraform, Kubernetes/EKS, Docker, EC2, networking, load balancers, and secrets management strongly preferred.
A track record of building or operating CI/CD, release automation, observability, alerting, and incident response systems.
Strong backend engineering judgment across service architecture, APIs, databases, async systems, queues, scaling limits, and production failure modes.
Experience designing systems for bursty workloads, long-running jobs, sandboxed execution, distributed workers, or high-concurrency services.
Background operating infrastructure for AI/ML, data-heavy, marketplace, workflow, developer-tools, or enterprise platforms.
Demonstrated focus on reducing cloud spend through better architecture, autoscaling, workload placement, caching, cleanup systems, or observability.
Ability to write clean, production-quality code; generic DevOps or infrastructure-only backgrounds without backend software engineering depth are not a strong fit.
Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.
On-site in Singapore. Candidates based in San Francisco are also considered for on-site work there. Candidates outside these locations, particularly in Europe, may be considered as fully remote independent contractors.
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