Software Engineer, Machine Learning Platform - Gen AI
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Seattle, WA
About this role
## Software Engineer, Machine Learning Platform - Gen AI ### About the Team DoorDash's GenAI Platform team builds shared infrastructure for safely bringing GenAI-powered products, agents, automation, and personalization to production across DoorDash, Wolt, and Deliveroo. The team runs frontier open-weight LLMs and VLMs (GLM, Qwen, Kimi, DeepSeek) with real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs—delivering significant cost and latency improvements. ### About the Role Join a high-leverage team building production infrastructure for Generative AI, focusing on open-weights model platforms spanning inference and fine-tuning. You'll work across model serving engines, fine-tuning pipelines, GPU autoscaling, batch systems, backend services, and observability. ### Key Responsibilities • Build infrastructure enabling GenAI ideas to move from prototype to production • Develop open-weights serving stack (real-time GPU endpoints, batch inference, fine-tuning) • Design scalable systems for model serving, GPU autoscaling, and fine-tuning • Optimize cost and latency of GPU inference while maintaining reliability • Build platforms supporting rapid experimentation with production standards • Partner with ML engineers, product teams, and data scientists across the organization ### Requirements • B.S., M.S., or Ph.D. in Computer Science or equivalent • 3+ years of industry software engineering experience • Strong backend fundamentals in Python and distributed systems • Production experience with services, APIs, data pipelines, or ML infrastructure at scale • Experience operating systems in production (observability, debugging, reliability, performance optimization) • Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production • Proficiency with AI coding tools (Claude, Codex, Cursor) across the full development lifecycle ### Nice to Haves • LLM inference engines (vLLM, SGLang, TensorRT-LLM) • Distributed fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA) • GPU performance optimization and quantization • Kubernetes, AWS/GCP, or serverless GPU platforms • LLM gateways, model routing, or cost attribution • Developer/internal platforms or AI agents in production ### Compensation **Base Salary Range:** $130,600 – $192,000 USD Compensation includes equity grants, 401(k) matching, 16 weeks paid parental leave, comprehensive healthcare, wellness benefits, and flexible PTO.
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