<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34&e=22630&t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181&utm_ptz=EST&utm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Team</strong></h2> <p>DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.</p> <h2><strong>About the Role</strong></h2> <p>You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.</p> <h2><strong>You’re excited about this opportunity because you will…</strong></h2> <ul> <li>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.</li> <li>Work on our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.</li> <li>Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases</li> <li>Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in.</li> <li>Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.</li> <li>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.</li> <li>Shape the future of DoorDash’s centralized GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalization.</li> </ul> <h2><strong>We’re excited about you because…</strong></h2> <ul> <li>B.S., M.S., or PhD. in Computer Science or equivalent</li> <li>3+ years of industry experience in software engineering</li> <li>Strong backend engineering fundamentals, especially in Python and distributed systems.</li> <li>Experience building production services, APIs, data pipelines, or ML infrastructure at scale.</li> <li>Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization.</li> <li>Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA).</li> <li>Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities</li> <li>Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software</li> </ul> <h2><strong>Nice To Haves</strong></h2> <ul> <li>Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production</li> <li>Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation</li> <li>GPU performance work — multi-node/distributed inference, KV-cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning</li> <li>Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems</li> <li>Experience with LLM gateways, model routing, vendor abstraction, or cost attribution</li> <li>Experience building d