1,826 Machine Learning Engineers jobs in the United Kingdom

Senior & Principal Applied AI Scientists – Automated Labs & Generative Modelling

Oxford/London | Hybrid | Full-time

• A top quartile salary designed to attract and reward world-class talent, plus comprehensive benefits (private medical, pension, income protection, enhanced holiday).
• Electric car scheme & lifestyle perks.
• A collaborative culture that values curiosity, resilience, and creativity.
• You'll be working with a world-class team.

Are you ready to join one of the most ambitious and rapidly scaling AI teams being built in Europe right now?

We’re assembling an elite group of researchers and engineers, drawing talent from FAANG, leading AI scaleups, and cutting-edge academic labs, to tackle some of the biggest scientific challenges of our time.

This is your chance to apply AI and automation at scale to reimagine how discovery happens in materials science, chemistry, and the life sciences.

Why this role matters

• Leveraging generative modelling, robotics, and autonomous experimentation to accelerate breakthroughs in chemistry, biology, and materials.
• Partner with leading scientists, engineers, and technologists from across the globe.
• Contribute to programmes that with strong humanitarian impact.
• Help build automated, data-driven research pipelines that move ideas from concept to solution faster than ever before.

What you’ll do

• Lead and contribute to the design and implementation of AI-driven discovery pipelines.
• Apply deep learning, foundation models, and generative approaches to problems in chemistry, materials, and the biosciences.
• Integrate with automated labs and high-throughput platforms to run closed-loop experiments.
• Collaborate with domain experts to ensure solutions address real scientific needs.
• Mentor and guide other scientists, setting the standard for technical excellence.

What we’re looking for

• PhD (or equivalent) in ML/AI, computational sciences, chemistry, materials, or related fields.
• Proven experience applying state-of-the-art ML models to real-world problems.
• Experience with generative modelling and/or automated laboratory systems.
• Strong track record of working with complex, large-scale scientific data.
• Excellent communication and the ability to work across disciplines.

Nice to have

• Experience in high-impact scientific domains (chemistry, drug discovery, materials, biology).
• Background in start-ups or fast-paced R&D environments.

Job Title: Contract Deep Learning Engineer

Location: London (Hybrid or On-site)

Contract Type: 6–12 months (with potential extension)

About the Role:

We are seeking a highly skilled Deep Learning Engineer to join our team on a contract basis. You will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems across domains such as computer vision, natural language processing, and time-series analysis.

Key Responsibilities:

• Design and implement deep learning models using frameworks such as PyTorch or TensorFlow.
• Collaborate with data scientists, ML engineers, and product teams to define model requirements and deployment strategies.
• Optimize model performance and scalability for production environments.
• Conduct experiments, evaluate model performance, and iterate based on results.
• Maintain clear documentation and contribute to knowledge sharing across the team.
• Stay up-to-date with the latest research and techniques in deep learning.

Required Skills & Experience:

• Proven experience in developing and deploying deep learning models in production.
• Strong proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow, Keras).
• Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.).
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow).
• Strong problem-solving skills and ability to work independently in a fast-paced environment.

Preferred Qualifications:

• MSc or PhD in Computer Science, Machine Learning, or related field.
• Experience with large-scale datasets and distributed training.
• Knowledge of model interpretability and responsible AI practices.

Benefits:

• Flexible working arrangements (hybrid or remote options available).
• Opportunity to work on cutting-edge AI projects.
• Collaborative and innovative team environment.

Job Title: Contract Deep Learning Engineer

Location: London (Hybrid or On-site)

Contract Type: 6–12 months (with potential extension)

About the Role:

We are seeking a highly skilled Deep Learning Engineer to join our team on a contract basis. You will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems across domains such as computer vision, natural language processing, and time-series analysis.

Key Responsibilities:

• Design and implement deep learning models using frameworks such as PyTorch or TensorFlow.
• Collaborate with data scientists, ML engineers, and product teams to define model requirements and deployment strategies.
• Optimize model performance and scalability for production environments.
• Conduct experiments, evaluate model performance, and iterate based on results.
• Maintain clear documentation and contribute to knowledge sharing across the team.
• Stay up-to-date with the latest research and techniques in deep learning.

Required Skills & Experience:

• Proven experience in developing and deploying deep learning models in production.
• Strong proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow, Keras).
• Solid understanding of neural network architectures (CNNs, RNNs, Transformers, etc.).
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow).
• Strong problem-solving skills and ability to work independently in a fast-paced environment.

Preferred Qualifications:

• MSc or PhD in Computer Science, Machine Learning, or related field.
• Experience with large-scale datasets and distributed training.
• Knowledge of model interpretability and responsible AI practices.

Benefits:

• Flexible working arrangements (hybrid or remote options available).
• Opportunity to work on cutting-edge AI projects.
• Collaborative and innovative team environment.

• Design, develop, and implement deep learning models and algorithms.
• Conduct research into new AI/ML techniques and technologies.
• Preprocess and analyze large datasets for model training.
• Optimize model performance for accuracy, efficiency, and scalability.
• Deploy machine learning models into production environments.
• Collaborate with cross-functional teams to integrate AI/ML solutions into products and services.
• Evaluate and select appropriate frameworks and tools for AI/ML development (e.g., TensorFlow, PyTorch).
• Stay current with advancements in the field of artificial intelligence and machine learning.
• Write clean, well-documented, and maintainable code.
• Contribute to the overall AI strategy and roadmap of the company.

• Conduct fundamental and applied research in deep learning, neural networks, and related AI fields.
• Develop and implement novel deep learning models and algorithms for complex problems.
• Design and execute experiments to validate research hypotheses and evaluate model performance.
• Analyze large datasets to extract insights and train state-of-the-art models.
• Collaborate with other researchers and engineers to integrate research findings into practical applications.
• Publish research findings in top-tier AI conferences and journals.
• Contribute to patent applications for novel technologies.
• Stay current with the latest research trends and advancements in AI and machine learning.
• Present research results to technical and non-technical audiences.
• Mentor junior researchers and contribute to a collaborative research environment.

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Strong publication record in leading AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR).
• Proven experience in developing and implementing deep learning models (e.g., CNNs, RNNs, Transformers).
• Proficiency in programming languages such as Python, and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of linear algebra, calculus, probability, and statistical methods.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and collaboration skills for effective remote teamwork.
• Ability to work independently and manage research projects proactively.
• Eligibility to work in the UK is required.

• Conduct cutting-edge research in deep learning and artificial intelligence.
• Develop and implement novel machine learning models and algorithms.
• Design, train, and evaluate deep neural networks for various applications.
• Explore and apply techniques in computer vision, NLP, and reinforcement learning.
• Collaborate with a team of researchers and engineers to translate research findings into practical solutions.
• Publish research findings in top-tier conferences and journals.
• Stay abreast of the latest advancements in AI and machine learning research.
• Contribute to the development of intellectual property and patent applications.
• Analyze large datasets to identify patterns and extract valuable insights.
• Prototype and test AI solutions, ensuring scalability and robustness.

• PhD or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Proven track record of research in deep learning, with publications in reputable venues.
• Expertise in at least one of the following areas: computer vision, natural language processing, reinforcement learning, or generative models.
• Proficiency in programming languages such as Python, and experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Strong analytical and problem-solving skills.
• Excellent communication and presentation skills, with the ability to explain complex concepts clearly.
• Ability to work independently and collaboratively in a hybrid work setting.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) is a plus.
• Experience with data engineering and MLOps is advantageous.