Top Machine Learning Jobs in London

Build and deploy AI systems for investment insights, focusing on ML and LLM-driven solutions, while ensuring high-quality data flow and outcomes.

The Senior ML Engineer will enhance fine-tuning methodologies, research inference optimization techniques, and re-implement LLM architectures focusing on distributed training and efficiency.

Lead software architecture, development, and team management for web applications and ML systems. Collaborate cross-functionally to drive innovation.

As a Machine Learning Scientist, you will research and deliver machine learning solutions, conduct experiments, and collaborate with stakeholders to improve Depop's app functionality for millions of users.

Develop ML-based models for alpha generation using high-frequency data, maintain data pipelines, optimize model performance, and collaborate with teams.

As a Junior Machine Learning Engineer, you will assist in model training, data collection, and evaluate AI applications, collaborating with ML engineers and researchers.

Develop scalable ML systems for driving behaviors, leading RL algorithms, and collaborating on technical projects to enhance Waymo's autonomous technology.

The Senior DevOps Engineer will design, implement, and optimize ML/AI pipelines using Azure Databricks, ensuring AI models are production-ready and scalable.

As an AI/ML Engineer, you will design and develop AI-powered applications, integrate AI features into BPM systems, and collaborate with teams to improve operational efficiency through innovative AI solutions.

As a Machine Learning Engineer Intern at Moloco, you will work on creating and optimizing models, and enhancing data pipelines while contributing as a full team member.

Develop and optimize AI models for specialized hardware, benchmark performance, and collaborate with various teams to innovate AI technology.

As a Sr. Solutions Architect, you'll develop customer engagement strategies, coach junior architects, and design complex data solutions using Databricks' Lakehouse platform.

The Senior Machine Learning Engineer will design and optimize data pipelines for autonomous driving research, ensuring high-quality datasets are used for model training and evaluation.

The Senior Machine Learning Engineer will design and deploy machine learning models and algorithms, collaborating with cross-functional teams to enhance educational products and transform Multiverse into an AI-first company.

Lead development and deployment of AI systems, design scalable ML software, mentor junior engineers, and collaborate with teams to solve client challenges.

Lead and manage a team of engineers on the Computer Vision team, overseeing development, deployment, and strategic evolution of AI-powered safety systems.

The job involves developing machine learning models, enhancing evaluation systems, collaborating with teams, and overseeing production and optimization of models for autonomous vehicles.

As a Machine Learning Researcher intern, you will collaborate with experienced ML researchers on projects related to systematic trading, applying novel ML techniques and engaging with large datasets.

As a Machine Learning Engineer, design and implement novel machine learning models, contribute to research papers, and work with cross-functional teams to deploy models in production environments.

The Lead Machine Learning Engineer will optimize pricing processes, manage ML model lifecycles, and collaborate with cross-functional teams to enhance predictive modeling capabilities.

Contribute to projects, implement research ideas, and run training and evaluation experiments for the Waymo Driver simulation.

This role involves developing and deploying machine learning models for fraud detection, collaborating with teams to ensure customer safety and business impact.

Lead the design and deployment of large-scale, automated systems using ML/AI technologies, ensuring service reliability and optimizing workflows within the organization.

Develop data and analytics products using machine learning and MLOps, build scalable language services, and automate testing and deployment in a SaaS environment.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.