Who we are
Panakeia is the world's first in silico multi-omics company, building AI solutions that power precision medicine in clinical settings, at scale. Our platform accelerates drug discovery and development for the medicines of tomorrow.
Our PANProfiler platform uses a biology-first AI approach to determine biomarker status across DNA, RNA, proteins, and metabolites directly from routine brightfield images of cells and tissues — no specialist equipment required. Two UKCA-marked clinical products derived from the platform are already deployed in hospitals across the UK and worldwide, giving clinicians faster, more accessible diagnostic insights to inform the treatment decisions that matter most.
The platform has been independently validated in peer-reviewed publications including Nature Communications Medicine, where it was recognised as one of the journal's top 25 papers of 2024, and results have been presented at leading clinical conferences including ESMO, ASCO, and AACR. We work closely with regulators — including the MHRA and FDA — to set the standard for safe, clinically-validated AI in healthcare.
Our fast-growing, multi-disciplinary team spans AI, molecular biology, and clinical science. If you're excited by technology that reaches patients directly and shapes how medicine is practised, this is where that work happens.
Role
As an ML Engineer at Panakeia, you will sit at the critical junction of research, engineering and product delivery. You will be responsible for building, training, and validating the machine learning models that power our platform and clinical products at scale.
You will own a clinical product end-to-end, from the initial exploratory phase through to delivery, while contributing to the platform and the wider set of solutions built on it. The role blends hands-on model development, rigorous experimentation, and turning ideas from research into working code — with a focus on models that generalise well and are robust for clinical use.
You will work across our multi-disciplinary team of AI, molecular biology, and clinical science, combining analytical rigour and engineering capability with the ability to communicate complex findings clearly to both technical and executive stakeholders.
Key responsibilities
- Product ownership: Take a leading hands-on role on one of our clinical products — running the modelling work, proposing approaches and improvements, and proactively surfacing problems and solutions rather than waiting for direction.
- Develop and improve models: Train, evaluate, and refine models across our platform — proposing architectures and training strategies, investigating underperformance, and ensuring they are optimised for specific customer requirements and real-world performance.
- Bring research into practice: Bring advances from the literature together with your own ideas to develop effective, practical solutions.
- Run experiments and validation studies: Design and run rigorous experiments and validation studies to test hypotheses and model performance against the high standards required for clinical use, and bring well-reasoned conclusions and recommendations on model readiness.
- Exploratory data analysis: Conduct deep exploratory analysis on new datasets to assess feasibility, produce insights, and help drive project strategy from zero to delivery.
- Work with data at scale: Efficiently process and handle large, high-dimensional image datasets as part of the model development workflow.
- Cross-functional collaboration: Work closely with cross-functional teams (Product, Research, Engineering) to take models from development through to delivery.
- Communicate results: Turn experimental and validation findings into clear conclusions and recommendations for technical colleagues and senior stakeholders, so results can inform product and clinical decisions.
Who we are looking for
Essential experience & mindset:
- One of the following backgrounds: 1) An MSc in Machine Learning, Computer Science, Mathematics, Statistics, Engineering, or a related field, with 2+ years of relevant industry experience in a fast-paced environment, ideally in a start-up. 2) A recent (or soon-to-complete) PhD in one of these fields, with some prior industry experience.
- Ability to iterate, experiment, and deliver solutions efficiently.
- Strong, hands-on programming experience in Python for building, training, and experimenting with machine learning models.
- Solid, hands-on experience with a deep-learning framework (e.g. PyTorch) — building and training models, not just familiarity.
- Experience in applying computer vision techniques to solve complex problems.
- Deep understanding of how machine learning models work, with the ability to build and validate robust, generalisable models for real-world scenarios.
- Familiarity with, or a strong interest in exploring, multi-modal learning and fusion models, to support the next generation of predictive modelling.
- Ability to read recent ML research and turn it into working, tested code.
- Proficiency in managing and processing large-scale, high-dimensional datasets, with efficient data handling within training workflows.
- Proactive, problem-solving mindset with a sense of ownership, able to spot opportunities for improvement and help move projects forward from idea to implementation.
- Capable of deep exploratory analysis to take a project from zero to delivery and produce insights for both technical and executive levels.
- Knowledge of designing and running rigorous experiments as well as validation studies to prove hypotheses.
- Adaptable and comfortable with evolving requirements and changing priorities, approaching new challenges with curiosity and openness.
- Desirable / makes you stand out:
- Ability to leverage AI productivity tools (e.g., Claude Code, Gemini) to accelerate research, coding, debugging, documentation and other workflows.
- Specific experience working with whole slide images and navigating the computational challenges associated with gigapixel-resolution pathology data.
- Experience working in a regulated environment (e.g., Medical Devices, HealthTech) or dealing with clinical data.
- Experience using cloud platforms (AWS/GCP) and containerisation (Docker) in day-to-day ML work.
- Experience with Large Language Models (LLMs) and Generative AI techniques and their applications in healthcare or data synthesis.
- Experience building models on multi-omics data (e.g. genomics, transcriptomics, proteomics), with an understanding of its practical challenges such as normalisation, batch effects, and missing modalities.
What we offer
- The chance to improve the lives of cancer patients worldwide by working on Panakeia’s cutting-edge AI-driven software platform and instrument-free precision diagnostic products.
- Massive growth potential in a fast-growing start-up, with opportunities to take on more responsibility as the company expands.
- Work with an inclusive, high-performing, and multi-disciplinary team of scientists, clinicians, and engineers.
- Hybrid working with flexible hours.
- Competitive base salary and equity participation through employee share options.
- 22 days holiday plus bank holidays, pension contribution, and an opportunity to shape the future of the company.
Success in your first year will look like:
- Taking ownership of one of our clinical products and delivering its key development and validation milestones.
- Contributing to projects across the platform and the wider set of products built on it, taking work from concept through to actionable outputs.
- Running experiments that directly inform team decisions or product development.
- Proactively improving workflows and processes to enhance team efficiency.
- Demonstrating growth, adaptability, and ownership by learning new tools or techniques and applying them successfully.
How to apply
Please fill this short application form and submit your CV.
This role does not include visa sponsorship; applicants must already have the right to work in the UK.
No agencies or recruiters. Unsolicited agency applications will not be considered.
At Panakeia, we welcome applications from all sections of society and are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, maternity or related condition or any other basis as protected by applicable law.