Own and scale the distributed infrastructure powering AI training environments. Optimize high-throughput production systems, improve reliability and observability, and operate close to hardware. Requires 3-8 years building large-scale distributed systems with Python and Linux kernel experience.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
About the Company
We're partnering with a frontier AI infrastructure company building the simulation environments that train the next generation of AI agents. As reinforcement learning becomes the key differentiator for capable AI systems, the bottleneck is no longer compute, it's high-quality training environments and infrastructure. The company is building the foundational infrastructure that transforms real-world data into scalable simulation environments, enabling frontier AI labs and enterprises to train more capable, reliable, and production-ready AI agents. Their work sits at the intersection of distributed systems, reinforcement learning, virtualization, and high-performance infrastructure.
Company Snapshot
- Closing an ~$18M Series A following strong early customer traction.
- Trusted by leading AI organizations including OpenAI and Amazon AGI Labs to power AI training infrastructure.
- Lean, highly technical team of ~10 engineers and researchers, offering exceptional ownership and direct founder collaboration.
- Building infrastructure at the intersection of Reinforcement Learning, Distributed Systems, Virtualization, and AI Infrastructure.
- Infrastructure is the core product, not internal tooling, with every optimization directly improving customer workloads and AI model performance.
- Engineers work directly with technologies including Firecracker microVMs, bare-metal compute, Linux kernel internals, distributed storage, and high-throughput systems.
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Why You Should Join
- Build critical infrastructure powering some of the world's most advanced AI research labs.
- Solve genuinely novel distributed systems challenges rarely found outside hyperscalers and frontier AI companies.
- Work directly with founders in a flat, high-agency engineering culture with exceptional technical talent density.
- Own infrastructure responsible for performance, reliability, cost optimization, and scalability at massive concurrency.
- Join at an inflection point as the company scales following its Series A financing.
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About the Role
You'll join as a Member of Technical Staff focused on owning and scaling the distributed infrastructure behind the company's AI training platform. You'll optimize high-throughput production systems, improve reliability and observability, operate close to the hardware, and partner directly with research engineers to build infrastructure that enables the next generation of AI models.
Qualifications
- 3–8 years of experience building and operating large-scale distributed systems or infrastructure platforms.
- Experience maintaining and scaling high-concurrency, production-critical infrastructure with strong ownership.
- Strong background in Python and experience with Rust or willingness to ramp quickly.
- Hands-on experience with Linux kernel internals, Firecracker microVMs, bare-metal compute, container orchestration, or virtualization technologies.
- Experience building distributed storage, queuing systems, observability, telemetry, or logging pipelines at scale.
- Background from a high-growth infrastructure startup, frontier AI company, or cloud platform team (e.g., OpenAI, Anthropic, Databricks, Cloudflare, HashiCorp, AWS, Google Cloud, CoreWeave, Modal, Fly.io, Together AI, or similar).
- Bachelor's or Master's degree in Computer Science or a related technical discipline preferred.
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Pay range and compensation package
- Compensation: $180K–$250K Base + Competitive Equity
- Location: San Francisco, CA
- Work Model: 100% Onsite (Relocation Support Available)
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