Software Engineer, Machine Learning Infrastructure - Gen AI

DoorDash USA•San Francisco, CA

4d•$137,100 - $201,600•Remote

About The Position

DoorDash’s GenAI Platform team, within Machine Learning Platform, builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. The mission is to increase the velocity of business impact from GenAI. The team owns core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, open-weights model serving and batch inference, guardrails, and cost attribution. This role involves joining a small, high-leverage team building production infrastructure for Generative AI at DoorDash. The engineer will work across backend services, ML infrastructure, agent/tool orchestration, evaluation systems, model serving, batch inference, and observability. This role is ideal for an engineer who enjoys building reliable platform primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.

Requirements

B.S., M.S., or PhD. in Computer Science or equivalent
4+ years of industry experience in software engineering
Strong backend engineering fundamentals, especially in Python and distributed systems.
Experience building production services, APIs, data pipelines, or ML infrastructure at scale.
Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization.
Familiarity with machine learning workflows such as inference, evaluation, feature/data pipelines, model serving, or experimentation.
Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities

Nice To Haves

Experience fine-tuning and serving open-weights LLMs in production
Experience building and deploying AI agents in production
Experience building and deploying MCP servers in production
Experience with LLM gateways, model routing, vendor abstraction, or cost attribution
Experience with eval systems, LLM observability, tracing, or LLM-as-judge workflows
Experience with RAG, search, vector databases, or retrieval pipelines
Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, or high-throughput batch systems
Experience building developer platforms, internal platforms, or self-serve infrastructure

Responsibilities

Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.
Work on production GenAI platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, open-weights model serving, batch inference, fine-tuning, guardrails, and cost attribution.
Design scalable systems for AI agents, MCP/tool orchestration, retrieval, batch inference, model serving, and evaluation workflows that power real customer and internal automation use cases.
Help product teams choose the right model and vendor strategy across closed-source and open-weight models, with reliability, fallback, observability, and cost controls built in.
Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.
Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.
Shape the future of DoorDash’s centralized GenAI platform, enabling the next generation of AI-powered products, agents, automation, and personalization.