Software Engineer, GenAI Platform

Software Engineer, GenAI Platform

Deliveroo

London, United Kingdom

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 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.