National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Machine Learning Engineer (Oxford)

In Technology Group
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer (Medical & Drug Discovery)

Oxford (2 days a week onsite)

Competitive (Up to £85,000, DOE)


Join a leading innovator in medical and drug discovery, using AI to accelerate healthcare breakthroughs. If you're passionate about applying machine learning to complex biological challenges, this is your chance to make a real impact.


Role Overview:

As a Machine Learning Engineer, you'll design and optimize AI models to advance biomedical research. You'll collaborate with data scientists, bioinformaticians, and scientific experts to transform large datasets into actionable insights.


Key Responsibilities:

  • Develop, train, and deploy ML models for protein structure, drug-target interactions, and biomarker discovery.
  • Build data pipelines for large biomedical datasets (genomics, clinical, molecular).
  • Implement deep learning models (e.g., CNNs, RNNs, transformers) for biological analysis.
  • Apply NLP to process biomedical literature and clinical data.
  • Collaborate with cross-disciplinary teams to ensure models meet scientific goals.
  • Continuously monitor and improve model performance.


Requirements:

  • Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Bioinformatics, or related field.
  • Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch).
  • Experience with bioinformatics tools (e.g., Biopython, RDKit).
  • Strong knowledge of statistical models, deep learning, and data preprocessing.
  • Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker).
  • Strong problem-solving and communication skills.


Desirable:

  • Experience with generative models (e.g., GANs, VAEs).
  • Knowledge of molecular docking, cheminformatics, or systems biology.
  • Understanding of regulatory and data privacy in healthcare AI.


Benefits:

  • Competitive salary and equity options.
  • Comprehensive health, dental, and vision coverage.
  • Opportunities for professional growth and research collaboration.
  • Flexible working environment, including remote options.


Help us drive the future of healthcare through AI-powered discoveries. Your work could lead to the next breakthrough drug or life-saving treatment.


Please apply ASAP to discuss further

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Part-Time Study Routes That Lead to AI Jobs: Evening Courses, Bootcamps & Online Masters

Artificial intelligence (AI) is reshaping industries at an unprecedented pace. From automating mundane tasks in finance to driving innovation in healthcare diagnostics, the demand for AI-skilled professionals is skyrocketing. In the United Kingdom alone, AI is forecast to deliver over £400 billion to the economy by 2030 and generate millions of new jobs across sectors. Yet, for many ambitious professionals, taking time away from work to upskill can feel like an impossible ask. Thankfully, part-time learning options have proliferated: evening courses, intensive bootcamps and flexible online master’s programmes empower you to learn AI while working. This comprehensive guide explores every route—from short tasters to deep-dive MScs—showcasing providers, course formats, funding options and practical tips. Whether you’re a career changer, a busy manager or a self-taught developer keen to go further, you’ll discover a pathway to fit your schedule, budget and goals.

Top 10 Mistakes Candidates Make When Applying for AI Jobs—And How to Avoid Them

Avoid the biggest pitfalls when applying for artificial intelligence jobs. Discover the top 10 mistakes AI candidates make—plus expert tips and internal resources to land your dream role. Introduction The market for AI jobs in the UK is booming. From computer-vision start-ups in Cambridge to global fintechs in London searching for machine-learning engineers, demand for artificial-intelligence talent shows no sign of slowing. But while vacancies grow, so does the competition. Recruiters tell us they reject up to 75 per cent of applications before shortlisting—often for mistakes that could have been fixed in minutes. To help you stand out, we’ve analysed thousands of recent applications posted on ArtificialIntelligenceJobs.co.uk, spoken with in-house talent teams and independent recruiters, and distilled their feedback into a definitive “top mistakes” list. Below you’ll find the ten most common errors, along with actionable fixes, keyword-rich guidance and handy internal links to deeper resources on our site. Bookmark this page before you hit “Apply”—it could be the difference between the “reject” pile and a career-defining interview.