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- Tasks: Design and scale ML infrastructure for high-performance AI research workflows.
- Company: Join a global leader in AI and health innovation.
- Benefits: Enjoy a competitive salary, hybrid work, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement in a cutting-edge field.
- Why this job: Make a real impact in AI while collaborating with top scientists and engineers.
- Qualifications: Experience in MLOps, cloud environments, and strong coding skills required.
The predicted salary is between 36000 - 60000 £ per year.
Location: Oxford, UK (Hybrid)
Organisation: Global AI & Health Innovation Programme
Employment Type: Full-time | Permanent
About the Role
We are seeking a ML Infrastructure Engineer to design, implement, and scale the infrastructure that supports high-performance machine learning and AI-driven research workflows. You will play a critical role in bridging the gap between data science, bioinformatics, and engineering — ensuring seamless, secure, and reproducible deployment of ML models in production and research environments. You’ll collaborate closely with AI Scientists, Data Engineers, and DevSecOps teams, building automation pipelines that accelerate model development and deployment across distributed, cloud-native systems.
Key Responsibilities
- Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
- Develop and automate workflows using Terraform, Kubernetes, Docker, and CI/CD toolchains (GitHub Actions, Jenkins, Argo, etc.).
- Manage scalable cloud-based compute environments (Oracle Cloud, AWS, or GCP) for AI workloads and data processing.
- Build and maintain feature stores, model registries, and versioning systems to ensure traceability and reproducibility.
- Implement data ingestion and pre-processing pipelines to support ML and bioinformatics workloads.
- Collaborate with security and DevSecOps teams to enforce best practices in access control, compliance, and governance.
- Support AI/ML researchers in model experimentation and infrastructure optimization.
- Monitor model and system performance, implement drift detection, and refine automated retraining pipelines.
- Contribute to the development of a modular, reusable ML platform architecture supporting multi-modal data (genomic, clinical, imaging, etc.).
Essential Skills and Experience
- Proven experience as an MLOps Engineer, Platform Engineer, or DevOps Engineer supporting ML or data science teams.
- Strong hands-on experience with containerization (Docker) and orchestration (Kubernetes).
- Expertise in Terraform, Infrastructure as Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure).
- Solid understanding of CI/CD pipelines and automated testing frameworks.
- Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools.
- Understanding of cloud security principles, IAM, and networking best practices.
- Proficiency in Python and Bash scripting for automation and tooling development.
- Version control with Git, and collaborative development practices.
Desirable Experience
- Exposure to bioinformatics or health data ecosystems (WGS, transcriptomics, clinical data).
- Knowledge of data governance and compliance frameworks (GDPR, ISO27001, HIPAA).
- Experience building monitoring dashboards for ML performance metrics.
- Familiarity with distributed training environments and GPU/TPU orchestration.
- Oracle Cloud Infrastructure (OCI) certification or equivalent.
Terms of Appointment
Applicants must have the right to work permanently in the UK and be within commuting distance of Oxford. Occasional travel may be required for collaboration across global sites.
ML Infrastructure Engineer employer: Hlx Life Sciences
Contact Detail:
Hlx Life Sciences Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Infrastructure Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. A friendly chat can sometimes lead to job opportunities that aren't even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving Docker and Kubernetes. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and Bash scripting. Practice common ML scenarios and be ready to discuss your experience with CI/CD pipelines.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace ML Infrastructure Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the ML Infrastructure Engineer role. Highlight your experience with MLOps, cloud environments, and any relevant tools like Docker and Kubernetes. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and health innovation. Share specific examples of your past work that relate to the responsibilities listed in the job description. Let us know why you’d be a great fit for our team!
Showcase Your Projects: If you've worked on any projects related to ML infrastructure or automation, make sure to mention them. We love seeing real-world applications of your skills, so include links to your GitHub or any relevant portfolios. It helps us understand your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Hlx Life Sciences
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Docker, Kubernetes, and Terraform. Brush up on your experience with cloud platforms like AWS or GCP, as you’ll likely be asked to discuss how you’ve used these tools in past projects.
✨Showcase Your MLOps Experience
Prepare specific examples of your previous work in MLOps, especially around building and maintaining pipelines. Be ready to explain how you’ve implemented CI/CD processes and automated workflows, as this will demonstrate your hands-on expertise.
✨Understand the Collaboration Aspect
Since the role involves working closely with AI Scientists and Data Engineers, think about how you’ve collaborated in the past. Prepare to discuss how you’ve bridged gaps between different teams and contributed to successful project outcomes.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the role and the company. Inquire about their current ML projects, the team dynamics, or how they approach model monitoring and performance metrics. This shows you’re genuinely interested and engaged.