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# Senior Software Engineer, GenAI Platform

- **Company:** Deliveroo
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Data Cleansing, Cursor, Software Debugging, Distributed Systems, Python (Programming Language), Software Engineering, Reinforcement Learning, Graphics Processing Unit (GPU), Autoscaling, Retrieval-Augmented Generation, Large Language Models, Claude Code, Distributed Inference, Generative AI, AI Coding Agents, Backend, Agentic-AI, Kubernetes, Information Technology, SGLang, Machine Learning Operations, TensorRT, VLLM, Model Inference, Serverless Computing
- **Published:** October 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2b85c1f35b30af5d

## About the Role

* BSc, MSc or PhD in Computer Science or equivalent
 * 5+ years of industry experience in software engineering
 * Deep backend engineering fundamentals, especially in Python and distributed systems.
 * Track record of designing and owning 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.
 * Deep 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).
 * Demonstrated technical leadership: leading design across ambiguous, fast-moving technical areas, mentoring engineers, and turning 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, * 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 optimisation, quantisation (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

## Description

You will join a small, high-leverage team building production infrastructure for Generative AI at Deliveroo and DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You'll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and 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., * Lead the design of infrastructure that helps teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.

 * Own and evolve 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.
 * Architect 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 - and raise the technical bar for - ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.
 * Set technical direction for the future of the company's centralised GenAI platform - including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post-training and agentic techniques - enabling the next generation of AI-powered products, agents, automation, and personalisation.