Software Engineer, Machine Learning Platform - Gen AI

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Join DoorDash's GenAI Platform team to build production infrastructure for Generative AI, focusing on the open-weights model platform spanning real-time GPU serving, high-throughput batch inference, and model fine-tuning. You'll work across inference engines, training pipelines, GPU autoscaling, backend services, and observability to push the cost/performance frontier of GPU inference. The role partners closely with ML engineers, product engineers, and data scientists across DoorDash, Wolt, and Deliveroo.

• Build infrastructure that moves GenAI ideas from prototype to production across the company
• Work on the open-weights serving stack: real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA)
• Design scalable systems for model serving, batch inference, GPU autoscaling, and fine-tuning
• Push the cost and latency frontier of GPU inference while providing reliability, fallback, observability, and cost controls
• Build platforms supporting rapid experimentation while meeting production standards for latency, scale, and SLOs
• Partner with ML engineers, product engineers, and data scientists to turn GenAI capabilities into platform primitives
• Shape emerging directions such as RLHF/RLVR, agent optimization, and post-training techniques

• B.S., M.S., or PhD in Computer Science or equivalent
• 3+ years of industry experience in software engineering
• Strong backend engineering fundamentals, especially Python and distributed systems
• Experience building production services, APIs, data pipelines, or ML infrastructure at scale
• Experience operating systems in production: observability, debugging, reliability, incident response, and performance/cost optimization
• Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production
• Ability to work across ambiguous, fast-moving technical areas
• Proficiency with AI coding tools (e.g., Claude Code, Codex, Cursor) across the software development lifecycle

• Experience with LLM inference engines and serving frameworks (vLLM, SGLang, TensorRT-LLM) in production
• Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA)
• GPU performance work: multi-node/distributed inference, KV-cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ)
• Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal)
• Experience with LLM gateways, model routing, vendor abstraction, or cost attribution
• Experience building developer or internal platforms
• Experience building and deploying AI agents or MCP servers in production
• Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases