Software Engineer, Machine Learning Platform - Gen AI

DoorDash USA•Sunnyvale, CA

21h•$130,600 - $192,000•Remote

About The Position

DoorDash’s GenAI Platform team, within Machine Learning Platform, is responsible for building the shared infrastructure that enables teams across DoorDash, Wolt, and Deliveroo to safely deploy Generative AI-powered products, agents, automation, and personalization into production. The team's mission is to accelerate the business impact derived from GenAI. A key aspect of this work involves self-hosting frontier open-weight Large Language Models (LLMs) and Vision-Language Models (VLMs), such as GLM, Qwen, Kimi, and DeepSeek. This includes managing real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs, which has resulted in significant cost and latency reductions (e.g., producing a billion embeddings at approximately 20x lower cost and serving visual models at roughly 72% lower cost). The team also manages core platform components like the LLM Gateway, Agent Gateway, evaluation infrastructure, guardrails, and cost attribution. You will join a small, high-leverage team focused on building production infrastructure for Generative AI at DoorDash. Your primary focus will be on our open-weights model platform, covering inference and fine-tuning, including real-time GPU serving, high-throughput batch inference, and model fine-tuning. You will engage with various aspects of the platform such as model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is well-suited for an engineer who thrives on optimizing the cost and performance of GPU inference and fine-tuning in a rapidly evolving technical landscape where product requirements, model capabilities, vendor ecosystems, and cost/performance trade-offs are constantly changing.

Requirements

B.S., M.S., or PhD. in Computer Science or equivalent
3+ 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.
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).
Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities.
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.

Nice To Haves

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

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 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.
Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases.
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.
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 — 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.