Backend Software Engineer (ML Infra)

What You'll Do Build & Scale Core Infrastructure - Design and implement backend systems that support large-scale ML workloads, including fine-tuning and reinforcement learning. - Build distributed training and inference pipelines that are efficient, fault-tolerant, and observable. - Develop internal developer tools and platforms that make it easier for ML engineers to train, evaluate, and deploy models.

Cloud & Systems Engineering - Work on cloud-native systems using containers and orchestration (e.g., Kubernetes). - Optimize systems for performance, reliability, and cost efficiency, especially for GPU-heavy workloads. - Implement monitoring, logging, and observability for long-running training jobs and production services.

Collaborate with ML Engineers - Partner closely with ML engineers to support evolving model architectures, training workflows, and evaluation needs. - Translate ML requirements into scalable backend and infrastructure solutions. Who You Are Required - 1-3 years of backend engineering experience, ideally working on production systems. - Strong fundamentals in distributed systems, networking, and backend architecture. - Experience building systems that scale under real load. - Comfortable working in Python and/or Go (or similar backend languages). - Excited to work on-site in San Francisco with a fast-moving early-stage team.

Strongly Preferred - Experience with or exposure to ML infrastructure or ML platforms. - Familiarity with GPU workloads, training pipelines, or inference systems. - Experience with containerization and orchestration (Docker, Kubernetes). - Contributions to or deep familiarity with ML infrastructure libraries such as: - Ray - vLLM - SGLang - or similar distributed ML systems Bonus - Computer science background from a top-tier program or equivalent demonstrated excellence. - Open-source contributions, research projects, or side projects in systems or ML infrastructure. - A track record of high ownership and technical curiosity.