Machine Learning Engineer at Scarlet

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

13 Mar, 26

Salary

0.0

Posted On

13 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Statistics, Deep Learning, Software Development, Data Exploration, Model Tuning, Dataset Creation, Evaluation Establishment, Production Deployment, AI Alignment, Agent Architectures, Concurrent Systems, Real-World Problem Solving, Regulated Environment Experience, GPU Memory Management, Software Shipping

Industry

Hospitals and Health Care

Description

Our mission is to hasten the transition to universally accessible healthcare. We are authorised by governments to assess and grant market access to medical AIs. Our groundbreaking approach enables the most innovative technology to reach patients safely and quickly. Scarlet is the pre-eminent authority on AI medical devices. We serve customers that matter. Companies building bleeding-edge medical AI systems choose Scarlet. We are proud to count the world’s best resourced and most ambitious companies building medical AI as customers. You will be joining a team with product-market fit, flowing data, and exponentially growing revenue. Come help us bring the next generation of healthcare to the people who need it. About the Team The Applied Machine Learning team at Scarlet works across the entire business to develop production tools that superpower our customers, and our internal teams, to achieve certification orders of magnitude faster and more reliably than previously possible. The Applied Machine Learning team works closely with medical doctors, AI researchers, and engineers to identify, prototype, validate, deploy and monitor AI systems that are fundamental to Scarlet’s mission. About the Role As a Machine Learning Engineer in Scarlet’s Applied Machine Learning team, you will have the opportunity to work with some of the brightest minds in AI medical devices. You’ll deploy state-of-the-art models in production environments, evaluate performance and iterate. If you're excited about building distributed agentic systems, fine tuning LLMs, and creating exceptional datasets, this role is your chance to make a significant impact on the quality of medical devices that go to market. You might, for example, work on: Agentic document search Build concurrent agent architectures to for rapid, multi‑agent evidence gathering across messy, distributed customer data sources. Deploy to production. Evals wired into real workflows Build evals that use our production assessment workflows to gather ground truth: IoU matching, precision/recall, agreement with human judgements, and other metrics that actually move the needle for certification timelines. Applied AI alignment Build customer support agents that delight customers and conform to our strict impartiality and objectivity commitments as a regulated certification agency. Who You Are Creator of datasets – You roll up your sleeves and build the dataset you wish existed, not wait for someone else to give you one. Establisher of evals – You think from first principles about statistics and how to evaluate ML systems, and you’re suspicious of benchmarks that don’t match reality. Tuner of models – ****Fine‑tuner, GPU memory leak fixer, agent swarm orchestrator Shipper of software – 3+ years experience shipping software to production Hacker – Track record of taking ambitious projects from concept to reality with whatever tools are at hand. Builder – You’re insatiably curious about real‑world problems and care that what you build has clear economic and human value. Desired: Solid understanding of machine learning, statistics, and deep learning fundamentals. Experience developing software for medical devices or in a regulated setting Proficient in exploring messy real-world data Application Process We won’t waste your time: Intro call with Alan (team lead) – mutual sanity check, space for your questions. Working session in our London office with Alan, Jamie (CTO), and team – walk through something you’ve built that you’re proud of; expect us to poke at your decisions and trade‑offs. Then riff with us on a real problem we're working on.

Responsibilities

As a Machine Learning Engineer, you will deploy state-of-the-art models in production environments and evaluate their performance. You will work closely with medical doctors, AI researchers, and engineers to develop tools that enhance certification processes.