Software Engineer, Inference

Ship new model architectures by integrating them into the inference engine.

Collaborate across research, engineering, and infrastructure to optimize model efficiency and deployments.

Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows.

Automate, test, and maintain inference services for maximum uptime and reliability.

Manage and optimize inference workloads across clusters and hardware providers, and scale deployments across thousands of machines.

Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.

One way the first 90 could unfold.

Days 1–30 — Immerse & Diagnose: Learn the inference stack, the fleets, and where reliability or utilization break.

Days 30–60 — Ship & Validate: Integrate a model or ship tooling/scheduling that improves uptime or GPU utilization.

Days 60–90 — Scale & Systemize: Harden deployment pipelines and scheduling across clusters and providers.

Strong Python and system-architecture skills.

Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar.

Experience with queues, scheduling, traffic control, and fleet management at scale.

Experience with Linux, Docker, and Kubernetes, and with orchestration, deployment, and scheduling.

Familiarity with Redis and S3-compatible storage.