Machine Learning Engineer

Responsibilities:

Custom Model Development: Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.

Third-Party Model Utilisation:Identify, tune and deploy third-party ML models, covering proprietary and open-source models.

Production Deployment:Manage the deployment of ML models into our production environments, ensuring they are scalable, reliable, and performant.

MLOps and Automation: Build and maintain robust MLOps pipelines for Continuous Integration/Continuous Delivery (CI/CD), model monitoring, and automated retraining.

Data Pipeline Construction: Collaborate with data engineers/stewards to build and optimise data pipelines that feed ML models, ensuring data quality and efficient processing for both training and inference.

Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.

Code and System Quality: Write clean, maintainable, and well-tested production-grade code. Uphold high software engineering standards across all projects.

Performance Tuning:Monitor and analyse model performance in production, identifying opportunities for optimization and iteration.

What You Need to Succeed (minimum qualifications):

Educational Background: A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field.

Programming Excellence: Advanced proficiency in Python and deep experience with core ML/data science libraries (, PyTorch, TensorFlow, scikit-learn, pandas, NumPy).

Software Engineering Fundamentals:Strong foundation in software engineering principles, including data structures, algorithms, testing, and version control (Git).

ML Model Deployment: Proven, hands-on experience deploying machine learning models into a production environment.

MLOps Tooling: Experience with MLOps tools and frameworks and containerisation technologies (Docker, Kubernetes).

Cloud Platform Proficiency: Practical experience with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their ML services (, Azure ML, Vertex AI).

DevSecOps: Proven experience with relevant DevSecOps concepts and tooling, including Continuous Integration/Continuous Delivery (CI/CD), Git SCM, Containerisation (Docker, Kubernetes), Infrastructure-as-Code (HashiCorp Terraform).

Machine Learning Theory: Solid understanding of the theoretical foundations of machine learning algorithms, including deep learning, NLP, and classical ML.

Problem-Solving: A pragmatic and results-oriented approach to problem-solving, with the ability to translate ambiguous requirements into concrete technical solutions.

Industry Experience:A broad understanding of life science, covering the business model, regulatory/compliance requirements, risks and rewards. An ability to identify and execute against opportunities within machine learning that directly support life science outcomes.

Communication: Excellent communication skills, capable of articulating complex technical decisions and outcomes to both technical and non-technical stakeholders.

Additional Information:

Travel:0-10%

Location: Hook, UK - Hybrid Work Environment