# Senior Software Engineer, GenAI Platform

**Company**: Deliveroo
**Location**: London
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/deliveroo/jobs/8205846?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_064eec08-faa

## 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.

Key responsibilities:

- 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.

Requirements:

- 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.

## Skills

### Required
- Python
- distributed systems
- backend engineering
- LLM inference
- fine-tuning
- open-weight models
- production services
- APIs
- data pipelines
- ML infrastructure
- observability
- debugging
- reliability
- incident response
- performance/cost optimization
- technical leadership

### Nice to have
- LLM inference engines
- serving frameworks
- distributed/multi-node fine-tuning
- training pipelines
- GPU performance
- Kubernetes
- cloud infrastructure
- GPUs
- serverless/elastic GPU platforms
- high-throughput batch systems
- LLM gateways
- model routing
- vendor abstraction
- cost attribution
- developer platforms
- internal platforms
- self-serve infrastructure
- AI agents
- MCP servers
- eval systems
- LLM observability
- tracing
- RAG
- search
- vector databases

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/deliveroo/jobs/8205846?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)