About the Role
We're a fast-growing AI/ML platform startup building infrastructure for training, evaluating, and aligning AI models within reinforcement learning environments. Our engineering team of ~15 includes competitive programming medalists, serial AI startup founders, and researchers published at top venues — and we're looking for a Platform Engineer to own the reliability, scale, performance, and developer experience of our core infrastructure.
This is a backend-architecture-heavy role with high ownership. Your work will directly determine how fast, reliable, and cost-effective our platform is to build on and run.
What You'll Do
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Own production uptime, latency, provisioning speed, infrastructure cost, and incident response for core platform services.
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Build and maintain AWS infrastructure using Terraform, Kubernetes/EKS, Helm, Docker, EC2, CodeBuild, ECR, S3, IAM, networking, and secrets management.
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Design and improve backend and platform systems for scale — capacity planning, autoscaling, queueing, backpressure, cleanup jobs, retries, and rollback paths.
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Define and improve dashboards, alerts, logs, traces, SLOs, runbooks, and on-call workflows so failures are detected, debugged, and resolved quickly.
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Build reliable CI/CD pipelines, release automation, environment management, and deployment workflows that improve developer productivity and reduce production risk.
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Write clean, maintainable production code to automate systems, improve backend services, and create internal developer tooling.
What We're Looking For
Required:
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2–4 years of experience owning production cloud infrastructure for a high-availability, user-facing platform, with accountability for uptime, performance, deployment safety, and cost.
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Deep hands-on experience with AWS and containerized systems; strong familiarity with Terraform, Kubernetes/EKS, Docker, EC2, load balancers, networking, and secrets management.
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Track record of building or operating CI/CD, release automation, observability, alerting, and incident response systems.
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Strong backend engineering judgment — able to reason about service architecture, APIs, databases, async systems, queues, scaling limits, and production failure modes.
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Ability to write production-quality code to automate infrastructure, improve backend services, and build internal tooling.
Nice to Have:
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Experience designing systems for bursty workloads, long-running jobs, sandboxed execution, distributed workers, or high-concurrency services.
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Background operating infrastructure for data-heavy, ML/AI, workflow, marketplace, developer-tools, or enterprise platforms.
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Demonstrated focus on reducing cloud spend through better architecture, autoscaling, workload placement, caching, or cleanup systems.
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Experience building internal platforms or developer tools that improve engineering productivity without hiding complexity.
We prioritize technical aptitude, ownership, and learning potential over years of experience.
Location
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San Francisco, CA (on-site): US-based candidates must be located in San Francisco.
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Singapore (on-site): Southeast Asia-based candidates must be located in Singapore.
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Fully remote (contractor): Candidates based elsewhere — particularly in Europe — may be considered as fully remote independent contractors.
Visa sponsorship is available.
Compensation & Benefits
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Salary: $150,000 – $250,000 USD annually (for full-time roles)
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Opportunity to have significant ownership and direct impact at an early-stage, well-funded AI infrastructure company.
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Work alongside a world-class technical team building foundational infrastructure for AI alignment and post-training data.
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