ML Engineer

We're looking for exceptional Machine Learning Engineers to join a scaleup Medical AI firm aiming to do wonders for drug trial success rates. With big investment and tie-ins secured, a large valuation already and top talent on board, this is one not to be missed! You’ll build the ML tools and infrastructure that allow researchers, scientists and pharma clients to deploy and scale foundation models / large medicine models effectively — no SaaS platform, just highly tailored solutions with real-world impact.

What You’ll Do

• Work as part of a high performing team of academic, AI and technology specialists to integrate and scale ML models in hybrid environments (on-prem + AWS cloud).
• Own and improve ML infrastructure: model deployment, training pipelines, inference tooling.
• Diagnose and optimise performance of large-scale ML models.
• Build and maintain experiment tracking, monitoring, and observability systems.
• Collaborate with SWE and infra colleagues to build tooling for data access, cleaning, and delivery.
• Contribute to the internal “toolbox” enabling repeatable, scalable ML deployment across client teams.
• Work closely with researchers and strategy teams to bridge cutting-edge models with real-world use.

Successful candidates will likely have a subset of the following:

• Strong academic background in Mathematics, Computer Science, or related field.
• Experience deploying ML models at scale in real-world, high-performance environments.
• Fluency in PyTorch (or similar) environments, with experience in multi-node training and scale-up workflows.
• Deep understanding of ML Ops best practices: experiment tracking, data/version control, reproducibility.
• Ability to diagnose and tune model performance (both training and inference).
• Comfort navigating hybrid infrastructure: some workloads will be on-prem, others cloud (large GPU clusters).
• Familiarity with distributed systems and container orchestration (e.g., Kubernetes, Ray).
• Experience working client-facing or in cross-functional teams — ideally within pharma/life sciences.
• A “get stuck in” attitude — this is a team of doers, not just architects.

Bonus Points For

• Familiarity with NVIDIA tools (e.g., NSight, Triton Inference Server) is a major plus.
• Multi-modal model experience.
• 3D imaging experience.
• Interest in or exposure to pharma and computational biology use cases.
• Experience in fast-paced environments (e.g., startups, hedge funds, advanced R&D orgs).

The Team

You’ll join a growing, technical-first team with SWE and infra colleagues lined up, and work alongside researchers and strategists to support 4–5 high-value clients at a time. The firm offers competitive salaries plus stock options and bonus schemes, and aims to provide a meritocratic environment where performance is rewarded in a big way.