Software Engineer, Machine Learning Infrastructure

SOFTWARE ENGINEER, MACHINE LEARNING INFRASTRUCTURE - GENERATIVE AI

ABOUT THE TEAM

Deliveroo'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.

ABOUT THE ROLE

You will join a small, high-leverage team building production infrastructure for Generative AI at Deliveroo and 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.

YOU'RE EXCITED ABOUT THIS OPPORTUNITY BECAUSE YOU WILL...

• Build the infrastructure that helps Deliveroo 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 the 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.

WE'RE EXCITED ABOUT YOU BECAUSE YOU HAVE...

• BSc, MSc, 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 buildin