At CoMind, we are developing a non-invasive neuromonitoring technology that will result in a new era of clinical brain monitoring. In joining us, you will be helping to create cutting-edge technologies that will improve how we diagnose and treat brain disorders, ultimately improving and saving the lives of patients across the world.

The Role:

The Data Science and ML team builds the algorithms that turn a novel optical brain signal into clinically meaningful outputs. The data comes from real ICU patients. The models will go into a regulated medical device used at the bedside.

We are looking for someone with deep theoretical ML knowledge and a track record of getting models out of research and into production.

At CoMind, all team members work at least 4 days per week from our new Kings Cross offices, plus a flexible work-from-home day.

Responsibilities:

• Own the design, benchmarking, and validation of ML model architectures for physiological signal inference, identifying the right approaches for a genuinely novel time-series problem

• Lead end-to-end delivery of ML workstreams, owning strategy, execution, and risk from problem definition through to production

• Develop and maintain robust training pipelines, evaluation frameworks, and reproducible experiment workflows

• Write production-quality Python code and prepare validated algorithm modules for handover to software engineering

• Work with physicists, neuroscientists, and clinicians to translate domain knowledge into modelling constraints, architecture choices, and training strategies

• Drive the team's engagement with the ML research literature, identifying and evaluating relevant advances across time-series, signal processing, and adjacent fields

• Contribute to publications, patent disclosures, and technical documentation that build CoMind's scientific credibility

• AI is fundamental to our culture. It's not just a tool, but a core part of how we work, collaborate, and innovate. We expect all team members to embrace AI in their daily work and continuously find new ways to use it effectively.

Skills & Experience:

• Deep theoretical knowledge of machine learning, including a strong grasp of the tradeoffs between model families across neural networks, tree-based methods, convolutional architectures, transformers, and state space models

• A track record of getting ML models into production, not just research environments

• Strong Python skills and comfort with the full ML engineering stack, from experiment tracking and training pipelines through to inference and deployment

• Experience with time-series or sequential data, including the practical challenges of non-stationarity, distribution shift, and small or noisy datasets

• Experience developing ML models for one of the following: hardware products, wearables, embedded systems, or edge devices

Nice to have:

• Experience applying ML to physiological, biological, or sensor data

• Background in or strong familiarity with signal processing concepts: spectral analysis, filtering, noise characterisation

• Familiarity with regulated development environments (SaMD, IEC 62304, or equivalent)

• Publications, patents, or congress presentations in a relevant technical area

Benefits:

• Company equity plan so all employees share in the success of the company

• Salary-sacrifice pension scheme

• Private medical, dental and vision insurance (medical history disregarded)

• Group life assurance at 4x annual income

• Comprehensive mental health support, including unlimited access to 1:1 sessions with trained professionals

• Unlimited holiday allowance (+ bank holidays) and one week of remote working per quarter

• Lunch voucher (£10) every day for JustEat and free dinner on those days where you need to work later

• Twice weekly deliveries of fresh fruit and an extensive selection of snacks and drinks

• YuLife subscription, allowing you to turn your daily steps and meditation into discounts at a range of stores

• Access to Udemy for upskilling and professional development.