Software Engineer, Machine Learning Infrastructure - Generative AI

Responsibilities

• Build infrastructure to move GenAI ideas from prototype to production
• Work on unified evals platform including SDKs, OpenTelemetry ingestion, and agent simulations
• Design scalable systems for LLM observability and trace/score ingestion
• Raise the quality bar for GenAI by providing trustworthy ways to measure model and agent quality
• Partner with ML engineers, product engineers, and data scientists to build durable platform primitives
• Shape the future of centralized GenAI platform through automated evaluation and post-training techniques

Requirements

• B.S., M.S., or PhD in Computer Science or equivalent
• 3+ years of industry experience in software engineering
• Strong backend engineering fundamentals in Python and distributed systems
• Experience building production services, APIs, data pipelines, or ML infrastructure at scale
• Experience operating systems in production (observability, debugging, reliability, performance optimization)
• Hands-on experience with evaluation, LLM observability, or measurement systems for ML/LLM products
• Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor)

Preferred Qualifications

• Depth in evaluation methodology (LLM-as-judge, drift detection, human-in-the-loop)
• Experience with LLM observability and tracing (OpenTelemetry)
• Experience building and deploying AI agents or MCP servers
• Experience with data pipelines, streaming ingestion, and analytical stores (SQL, OLAP)
• Experience with LLM gateways, model routing, or cost attribution
• Experience with Kubernetes, AWS, or GCP
• Experience with RAG, search, vector databases, or LLM fine-tuning

About the Company

At DoorDash, our mission is to empower local economies. We are a technology and logistics company that is expanding from food delivery to providing any and all goods through rapid innovation and impactful decision-making.