Software Engineer, Full-Stack ML Infrastructure

What you'll do

• Improve development environments, automated testing, CI/CD, and deployment workflows.
• Build reproducible experiment and evaluation workflows, including tools for launching jobs, tracking results, and troubleshooting failures.
• Develop observability and shared platform services for diagnosing issues and understanding AI-system usage and costs.

What they ask for

• 2–3 years of software engineering experience, including hands-on work with backend services, full-stack applications, internal tools, or deployment automation.
• Ability to build services and automation in Python or TypeScript, work with APIs and databases, and build interfaces with React or a similar framework.
• Practical experience deploying applications with containers and a cloud platform such as AWS, working with CI/CD, and using logs or metrics to investigate problems.
• Some hands-on exposure to AI or ML workflows, such as integrating model APIs, running evaluations, tracking experiments, or operating model-backed applications.

Nice to have

• Experience with tools such as MLflow, Langfuse, Temporal, Terraform, Kubernetes, or OpenTelemetry.
• Experience with GPU workloads on Kubernetes, model serving with vLLM or SGLang, platform services, or developer tools.

Tools

Python, TypeScript, React, AWS, MLflow, Langfuse, Temporal, Terraform

AI focus

Build and operate infrastructure and tools for AI/ML development, experiments, evaluations, model services, observability, and usage metering.