Design and build scalable systems for real-time fraud detection, including data pipelines and backend services in Go. Develop and deploy ML models while ensuring high standards of security, privacy, and observability.
Remote Machine Learning Engineer Jobs in United Kingdom (26)
Search = Machine Learning Engineer
Remote in = United Kingdom
Design and optimize AI models for image search and online content monitoring using computer vision and NLP. Build scalable data pipelines and continuously monitor production model performance to improve accuracy.
The core responsibilities involve joining the team to work on machine learning engineering tasks. Further details about specific duties are not provided in the description.
Design and build systems to improve the efficiency of ML training and inference workloads while optimizing GPU and resource utilization. Lead cross-functional initiatives to drive technical strategy for ML platform scalability, reliability, and cost efficiency.
Senior Machine Learning Engineer, Ads Foundational Representations
Reddit
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Full Time
·
2 Days Ago
Reddit
Lead the full-cycle execution of ML projects to develop signals and representations for Reddit's core entities. This includes building embedding models, developing inference pipelines, and conducting end-to-end experimentation to improve ads delivery and ranking.
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Senior Software Engineer - Machine Learning (Marketplace Dynamics) (Remote - United Kingdom)
Yelp, Inc
·
Full Time
·
4 Days Ago
Yelp, Inc
Identify and solve challenging marketplace dynamics problems by designing predictive models and conducting causal analysis. Productionize and automate ML model pipelines within Python services to drive business impact.
Lead the development of AI-powered solutions and enterprise-grade LLM systems to solve complex business challenges. Partner with stakeholders to define strategic roadmaps and mentor other machine learning engineers on design and coding standards.
Design and deploy ML models for document classification, entity extraction, and summarization of complex financial documents. Build scalable production-ready ML services and contribute to the MLOps stack including CI/CD and evaluation frameworks.
Design, build, evaluate, and ship agentic based features and interactive experiences. Collaborate with cross-functional teams to build new product features that connect artists and fans in personalized ways.
You will own the end-to-end development of critical ML subsystems, including data preparation, training, and production deployment. You will collaborate with research and engineering teams to build reliable, scalable AI systems that meet performance and latency targets.
You will be responsible for building and owning end-to-end machine learning pipelines, including training, evaluation, inference, and production deployment. You will collaborate with research and application teams to integrate scalable, high-performance AI systems into products.
Evaluate and analyze LLM performance while architecting and building inference and training pipelines. You will contribute to hands-on design, model training, and deployment strategies within a technical greenfield project.
You will own the end-to-end deployment of machine learning models, including data validation, model selection, and performance monitoring. You will also collaborate with clients to understand their business needs and ensure the forecasting system delivers actionable results.
Machine Learning Engineer II - Behavioral Security Products 13 APPLICANTS
Abnormal
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Full Time
·
11 Weeks Ago
Abnormal
The role involves developing machine learning algorithms for behavioral modeling and cybersecurity attack detection, as well as collaborating with cross-functional teams to implement effective solutions. Additionally, the engineer will monitor and improve production models and participate in code reviews.
Own the technical direction for pricing machine learning, defining the strategy and roadmap for the pricing engine. Build ML systems for price optimisation and solve imbalance problems while bridging modelling and production.
The role involves researching and building deep learning systems for expressive speech generation and developing state-of-the-art generative systems for video and audio synthesis. As a senior member, the engineer will shape core infrastructure, set best practices, and mentor junior staff while delivering high-quality ML pipelines in production.
The role involves designing modular and scalable real-time data pipelines to manage large datasets and implementing custom Machine Learning algorithms within a low-latency environment. Responsibilities also include working on microservice architectures that handle the training, inference, and monitoring of thousands of ML models concurrently.
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You will work end-to-end on new Agentic AI Agents and manage the safe release of features to customers. Your role will involve a combination of UXD, infrastructure, and machine learning challenges.
The Senior Platform Engineer will design, build, and maintain the infrastructure and tools for training, evaluating, deploying, and serving Machine Learning models. They will collaborate with ML teams to identify needs and create solutions.
The Senior Platform Engineer will design, build, and maintain the infrastructure and tools for training, evaluating, deploying, and serving Machine Learning models. They will collaborate with ML teams to identify needs and create solutions.
The team is responsible for designing, building, and maintaining the infrastructure and tools that enable other teams to train, evaluate, deploy, and serve Machine Learning models and features at scale. This involves working closely with ML teams to identify needs and build magically simple solutions using backend engineering experience.
You will build and maintain foundational AI gateways and inference services for Kraken, ensuring reliable access to ML and generative AI models. Additionally, you will architect internal evaluation tooling and act as a technical mentor for other teams.
Lead the development and scaling of the Wildfire Fuel Detection Model, focusing on understanding vegetation structure and wildfire risk. Collaborate with data scientists and product teams to ensure models are accurate and production-ready.
Design and scale machine learning systems to improve ad relevance and optimization. Collaborate with customers in Mandarin to deliver data-driven ML solutions.
Focus on improving e-commerce search relevance through LLM-based evaluation and real-time quality improvements. Develop automated monitoring pipelines and agentic systems to detect degradations and provide actionable product recommendations.
Lead and manage cross-functional teams of machine learning engineers and data scientists to deliver ML-driven product capabilities. Accountable for technical direction, talent growth, and strategic partnership with product managers to balance short- and long-term goals.
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