115 Machine Learning Engineers jobs in London

Working Pattern : Hybrid (1-2 days per week in office)

Location : London

As a company for whom AI is the product, it should be no surprise that our Data Science team is at the heart of everything we do at Sprout - building innovative products, researching new techniques for using Artificial Intelligence in claims automation, and pushing the boundaries of what our product can achieve. 

As a globally dispersed team, our Data Science team brings together a diverse range of expertise and backgrounds; what unites us is a desire to learn, a mastery of our discipline, strong mathematical and statistical skills, and software engineering prowess. We typically specialise in fields such as Computer Vision, LLMs and Deep Learning. 

Our Machine Learning Engineers are responsible for all aspects of the AI lifecycle, from understanding business problems, preparing training data, assisting the data scientists in designing and building models, and deploying them into production. We work in cross-functional squads, so you will work collaboratively with Data Scientists, Software Engineers, Product and Engagement Managers on your designated project.

You will not only work on the development and productionisation of sophisticated ML products, but also shape the future of our AI capabilities. You will have the opportunity to mentor junior team members, influence strategic decisions, and directly impact our customers’ experiences.

If you are passionate about transforming industries with AI and want to work with an innovative, ambitious team, we would love to hear from you. Apply now and help shape the future of claims automation.

• Develop features for our state-of-the-art claims automation platform
• Build and deploy machine learning algorithms and models to production within product teams
• Provide technical guidance and input on the design and implementation of machine learning algorithms
• Support with customer PoVs and onboarding
• Understand business problems and product requirements and help translate these into technical solutions
• Execute and deliver full AI/ML solutions from sourcing training data, design and implementing state-of-the-art machine learning models, testing, benchmark and product-driven research for model performance improvement, to shipping stable, tested, performant code in an agile environment.
• Work closely with Product Managers to help shape the product roadmap from a Data Science perspective
• Contribute to Data Science strategy and the technical roadmap in conjunction with our Head of AI
• Proactively seek to improve the way that Data Science operates at Sprout.ai
• Support the education of the business and customers on how our Data Science teams work
• Stay updated on the latest trends and advancements in Artificial Intelligence.

• Technical proficiency
• You write production-grade, scalable Python code, ensuring that your models are robust, maintainable, and optimised for performance.
• Comfortable with PyTorch
• Knowledge of Transformer-based models
• Knowledge of Large Language Models (LLMs)
• Proven experience of having delivered successful machine learning projects into production 
• Strong understanding of software development fundamentals, in particular deploying models to production and how to set up pipelines.
• Demonstrate expertise in deep learning for computer vision, natural language processing, reinforcement learning etc
• Displays in depth knowledge in machine learning best practices, scalable training and deployment, model introspection and evaluation
• Understanding of the fundamentals in Mathematics, Statistics and Data Analysis
• Experience working in an Agile environment and knowledge of how Agile methodologies can be applied to Data Science teams in terms of process, practice, team culture and the delivery of work
• Ability to convert customer requirements or business challenges into well-defined machine learning solutions
• We are using many technologies day to day such as various AWS services, GCP, Kubernetes, Ray Serve, Kubeflow, and ReTool. Any experience in these areas would be a bonus

Working Pattern : Hybrid (1-2 days per week in office)

Location : London

As a company for whom AI is the product, it should be no surprise that our Data Science team is at the heart of everything we do at Sprout - building innovative products, researching new techniques for using Artificial Intelligence in claims automation, and pushing the boundaries of what our product can achieve. 

As a globally dispersed team, our Data Science team brings together a diverse range of expertise and backgrounds; what unites us is a desire to learn, a mastery of our discipline, strong mathematical and statistical skills, and software engineering prowess. We typically specialise in fields such as Computer Vision, LLMs and Deep Learning. 

Our Machine Learning Engineers are responsible for all aspects of the AI lifecycle, from understanding business problems, preparing training data, assisting the data scientists in designing and building models, and deploying them into production. We work in cross-functional squads, so you will work collaboratively with Data Scientists, Software Engineers, Product and Engagement Managers on your designated project.

You will not only work on the development and productionisation of sophisticated ML products, but also shape the future of our AI capabilities. You will have the opportunity to mentor junior team members, influence strategic decisions, and directly impact our customers’ experiences.

If you are passionate about transforming industries with AI and want to work with an innovative, ambitious team, we would love to hear from you. Apply now and help shape the future of claims automation.

• Develop features for our state-of-the-art claims automation platform
• Build and deploy machine learning algorithms and models to production within product teams
• Provide technical guidance and input on the design and implementation of machine learning algorithms
• Support with customer PoVs and onboarding
• Understand business problems and product requirements and help translate these into technical solutions
• Execute and deliver full AI/ML solutions from sourcing training data, design and implementing state-of-the-art machine learning models, testing, benchmark and product-driven research for model performance improvement, to shipping stable, tested, performant code in an agile environment.
• Work closely with Product Managers to help shape the product roadmap from a Data Science perspective
• Contribute to Data Science strategy and the technical roadmap in conjunction with our Head of AI
• Proactively seek to improve the way that Data Science operates at Sprout.ai
• Support the education of the business and customers on how our Data Science teams work
• Stay updated on the latest trends and advancements in Artificial Intelligence.

• Technical proficiency
• You write production-grade, scalable Python code, ensuring that your models are robust, maintainable, and optimised for performance.
• Comfortable with PyTorch
• Knowledge of Transformer-based models
• Knowledge of Large Language Models (LLMs)
• Proven experience of having delivered successful machine learning projects into production 
• Strong understanding of software development fundamentals, in particular deploying models to production and how to set up pipelines.
• Demonstrate expertise in deep learning for computer vision, natural language processing, reinforcement learning etc
• Displays in depth knowledge in machine learning best practices, scalable training and deployment, model introspection and evaluation
• Understanding of the fundamentals in Mathematics, Statistics and Data Analysis
• Experience working in an Agile environment and knowledge of how Agile methodologies can be applied to Data Science teams in terms of process, practice, team culture and the delivery of work
• Ability to convert customer requirements or business challenges into well-defined machine learning solutions
• We are using many technologies day to day such as various AWS services, GCP, Kubernetes, Ray Serve, Kubeflow, and ReTool. Any experience in these areas would be a bonus

At TWG Group Holdings, LLC (“TWG Global”), we drive innovation and business transformation across a range of industries, including financial services, insurance, technology, media, and sports, by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees. 

We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development. 

You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation. 

At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses. 

The Role:

As a Principal Machine Learning Engineer (UK), you will be embedded in the UK Data Science team and play a critical role in accelerating delivery of AI solutions. Reporting to the Head of the UK AI & Data Science team, you will work alongside Data Scientists to take prototypes and translate them into reliable, production-ready services for business stakeholders.

This is a hands-on technical leadership role: you will set direction, architect solutions, and mentor peers. The remit is squarely focused on last-mile delivery — taking prototypes built by Data Scientists and making them usable in real business settings by packaging them into services or APIs, wiring them into data sources, building lightweight feature and inference pipelines, and adding basic monitoring and retraining logic. You will accelerate pilot deployments so stakeholders can see value quickly, and then hand the solution off to the Central Engineering and Data team, who are responsible for firm-wide platforms, scaling, and long-term support.

The ideal candidate will also bring applied data science skills and be comfortable moving between ML engineering and data science work. You should be able to contribute to model development and analysis when needed, in addition to owning deployment and operationalization.

Responsibilities:

• Translate data science prototypes into production-ready pilot ML services tailored to business use cases.
• Build lightweight pipelines (feature engineering, model packaging, inference services) that integrate smoothly with central platforms while meeting immediate delivery needs.
• Champion pragmatic MLOps practices (CI/CD for ML, monitoring, observability) to improve reliability without duplicating central engineering’s enterprise frameworks.
• Partner closely with Data Scientists to operationalize models, and collaborate with central engineering to plan handoffs of successful pilots for hardening and scale.
• Apply emerging ML engineering techniques (LLM deployment, RAG, vector databases) to accelerate delivery of applied projects.
• Develop reusable components and lessons learned that central teams can adopt into firm-wide platforms.
• Ensure ML workflows comply with governance, audit, and regulatory requirements.
• Collaborate with central Engineering, Data, Product, and Security teams to ensure alignment with firm-wide platforms and standards.
• Provide technical mentorship to ML engineers, raising the bar for applied delivery and model deployment.
• Flex into data science tasks when needed: feature engineering, model experimentation, and analytical insights, reflecting the versatility required in a fast-moving team.

Requirements

• 8+ years of experience designing, building, and deploying ML systems in production.
• Proven track record of leading ML engineering projects from prototype to production delivery.
• Deep expertise in modern ML frameworks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow).
• Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++).
• Strong knowledge of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML) and containerized deployments (Kubernetes, Docker).
• Hands-on experience with ML pipelines, distributed training, and inference scaling.
• Familiarity with monitoring stacks (Prometheus, Grafana, ELK, Datadog).
• Experience in regulated industries (finance, insurance, healthcare) with compliance and governance needs.
• Strong communication and collaboration skills, with the ability to mentor others and influence technical direction.
• Working knowledge of data science techniques (e.g., supervised/unsupervised ML, model evaluation, causal inference, feature engineering).
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related technical field (PhD a plus).

Preferred Experience:

• Experience integrating with Palantir platforms (Foundry, AIP, Ontology) as a user/consumer.
• Practical exposure to LLM and GenAI delivery (fine-tuning, RAG, vector search, inference).
• Experience optimizing GPU clusters or distributed training workloads.
• Familiarity with graph databases (Neo4j, TigerGraph) in applied ML contexts.

Benefits

• Work at the forefront of AI/ML innovation in life insurance, annuities, and financial services.  
• Drive AI transformation for some of the most sophisticated financial entities.  
• Competitive compensation, benefits, future equity options, and leadership opportunities. 
  

This is a hybrid position based in the United Kingdom.

We offer a competitive base pay + a discretionary bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits. 

TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. 

Coders Connect has partnered with an innovative start-up who are embarking on an incredible journey with an AI-powered song-generation platform.

A small, dynamic team in London is revolutionizing the music industry by creating fresh and captivating songs across various genres. As a Machine Learning Engineer, you'll dive into the heart of this groundbreaking project, working on the Desinging and implementation of Data Pipelines, research, finetune and develop state-of-the-art generative AI models. Embrace the endless possibilities as the platform extends to the Web (WASM), Android, iOS, and even virtual reality!

If your passion for music harmonizes perfectly with your Machine Learning skills, this opportunity is tailor-made for you! Don't miss out on this thrilling adventure. Apply and shape the future of AI music production!

• Designing, building, and deploying sophisticated machine learning models and algorithms.
• Developing and maintaining robust ML pipelines for data preprocessing, model training, evaluation, and deployment.
• Conducting experiments to test and optimise model performance using various metrics.
• Implementing MLOps best practices to ensure the scalability, reliability, and maintainability of production ML systems.
• Collaborating with data scientists and domain experts to define problem statements and identify relevant data sources.
• Writing clean, efficient, and well-documented code in Python or other relevant programming languages.
• Staying abreast of the latest research and advancements in machine learning and artificial intelligence.
• Contributing to the technical roadmap and strategic direction of the ML team.
• Mentoring junior engineers and sharing knowledge across the team.
• Working effectively in a distributed, remote-first environment, with strong communication and collaboration skills.

• Design, implement, and deploy machine learning models and algorithms for various NLP tasks, focusing on scalability and performance.
• Develop and maintain robust data pipelines for training and evaluating NLP models.
• Collaborate with data scientists, software engineers, and product managers to integrate ML solutions into production systems.
• Optimize existing ML models for improved accuracy, efficiency, and resource utilization.
• Conduct thorough experimentation, hyperparameter tuning, and model evaluation.
• Stay abreast of the latest research and advancements in NLP, deep learning, and machine learning.
• Contribute to the development of best practices for MLOps, including model monitoring, deployment, and retraining strategies.
• Write clean, maintainable, and well-documented code in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
• Participate in code reviews and knowledge-sharing sessions with the engineering team.
• Troubleshoot and resolve issues related to ML model performance and deployment.

• Design, build, and deploy scalable machine learning models and systems.
• Develop and maintain robust ML pipelines for data processing, training, and inference.
• Research and implement state-of-the-art ML algorithms and techniques.
• Collaborate with cross-functional teams to define and implement AI solutions.
• Mentor junior engineers and promote best practices in ML development.
• Optimize model performance, scalability, and reliability.
• Evaluate and select appropriate ML frameworks and tools.
• Communicate technical findings and recommendations to stakeholders.
• Stay current with advancements in AI, ML, and related fields.
• Contribute to the overall technical strategy and roadmap.

• Master's or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• 8+ years of hands-on experience in machine learning engineering.
• Expertise in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
• Strong understanding of various ML algorithms, including deep learning.
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Proficiency in big data technologies (e.g., Spark, Hadoop).
• Excellent problem-solving and analytical skills.
• Strong communication and leadership abilities.
• Experience in a remote work environment is highly desirable.