Staff Machine Learning Engineer - HealthTech - Hybrid

An AI‑driven healthcare technology company is seeking a Staff Machine Learning Engineer to lead applied research and model development for large‑scale, image‑based ML systems operating in regulated environments.

This is a hands‑on senior individual contributor role with significant influence over model architecture, experimentation strategy, and ML best practices, working at the intersection of research‑quality machine learning and real‑world production deployment.

The Opportunity

You’ll play a central role in advancing next‑generation deep learning models that operate on large, complex imaging datasets, helping shape technical direction while collaborating closely with engineers, scientists, and domain experts.

The role suits someone who enjoys:

• Owning end‑to‑end model development
• Operating in high‑bar, regulated environments
• Balancing research innovation with production rigor
• Acting as a technical leader and mentor without needing people‑management responsibilities

What You’ll Be Doing

• Lead the design, training, and evaluation of advanced deep learning models
• Develop scalable training pipelines for large image datasets
• Drive performance improvements via experimentation, architectural innovation, and robust evaluation
• Establish and promote best practices for model development, reproducibility, and validation
• Collaborate cross‑functionally with engineering, product, and domain specialists
• Partner with infrastructure teams to optimise GPU usage, distributed training, and deployment workflows
• Mentor and provide technical guidance to other ML engineers and data scientists
• Contribute to external publications and internal IP where relevant

What We’re Looking For

• PhD in Computer Science, Machine Learning, EE or similar with 3 years industry experience, or MSc with 6 years of applied ML experience
• Strong foundations in machine learning with deep experience in modern deep learning methods
• Hands‑on expertise with Python and PyTorch
• Experience building and training models on large‑scale image datasets
• Proven ability to take models from research into production
• Comfortable working within regulated or high‑compliance environments
• Strong communication skills and ability to work cross‑functionally

Nice to Haves

• Experience with self‑supervised or representation learning
• Distributed training across multi‑GPU / multi‑node systems
• Familiarity with ML workflow orchestration tools
• Exposure to regulated ML systems (e.g. medical, biotech, safety‑critical software)
• Track record of publications in ML or applied research venues

Why This Role

• Staff‑level ownership with genuine influence over core ML systems
• Real‑world impact deploying ML at scale, not just experimentation
• Well‑funded organisation with production systems already live
• A rare opportunity to operate at the intersection of research, engineering, and regulated deployment