Software Engineer, Machine Learning Infrastructure

United States Digital Space LLC; Greater London; Hybrid; GBP 120,000 - 170,000; Full time

Job summary

We are hiring a Software Engineer, Machine Learning Infrastructure to join a small, high-leverage team building production GenAI infrastructure at scale. The role focuses on real-time GPU serving, high-throughput batch inference, and model fine-tuning for open-weight platforms.

You will collaborate across model serving, inference engines, training pipelines, and observability, pushing cost/performance frontiers while meeting latency and reliability targets.

Qualifications

• Experience building production services, APIs, data pipelines, or ML infrastructure at scale.
• Strong backend engineering fundamentals with Python and distributed systems.
• Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production.

Responsibilities

• Build infrastructure for GenAI ideas from prototype to production to accelerate business impact.
• Work on the open-weights serving stack including real-time endpoints and batch inference.
• Design scalable systems for model serving, batch inference, GPU autoscaling, and fine-tuning.

Skills

Backend engineering

Python

Distributed systems

Production services

Observability

Cost optimization

LLM inference

Fine-tuning

GPU utilization

AI tooling

Education

BSc/MSc/PhD in Computer Science or equivalent

Tools

Kubernetes

AWS/GCP

Modal

Git

Job description

Software Engineer, Machine Learning Infrastructure - Generative AI

About the Team

the company's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and the company 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 20x 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 the company 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 the company 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 the company 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

Diversity, Equity and Inclusion

At the company, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong.

We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio‑economic background, religion, or belief.

If you have a disability or long‑term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience.