76 to 100 of 230 Algorithms Jobs in London

Research Engineer in Greater London. The role involves supporting researchers with large-scale ML pipelines, integrating cutting-edge research with production, and designing ML algorithms in Python. Candidates with a Master's or PhD in Computer Science and 4+ years of relevant experience, particularly in deep learning ...

market opportunities. As a Junior Quant Developer (0–4 years of experience), you will: Develop and enhance high-performance trading systems. Optimise execution algorithms for systematic trading. Contribute to research and simulation frameworks. Key Requirements Bachelor's degree in Computer Science or closely related field Interesting internship(s) within trading ...

Strong proficiency in any OO language (they are agnostic) - (low-latency or systems-level experience preferred) Deep understanding of data structures, algorithms, and computer architecture Proven ability to tackle complex challenges with initiative and agility in high-pressure environments Apply now or reach out directly: hhandelman@hunterbond.com for more details ...

full development lifecycle, including testing and deploying into production. You will also contribute to research and evaluation of new AI/Machine Learning algorithms and stay up to date with industry trends and emerging technologies, collaborating to design new features and functionality. Location/WFH: You'll join colleagues ...

/or C++ , depending on background) Experience building tools for quant research, systematic strategies, or data-driven trading Solid understanding of data structures, algorithms, and software engineering best practices Familiarity with market data, time-series analysis, or trading system design Ability to work independently while collaborating closely with a small ...

Exceptional programming skills in Python and/or C++, with a focus on writing clean, performant production code Strong understanding of core CS fundamentals (algorithms, distributed systems, profiling, testing) Experience in a systematic trading environment or quantitative data domain is highly advantageous, though not essential A problem-solver with ...

What you'll be doing: Have a critical role in architecting, implementing, and maintaining production‐grade, low‐latency ML services for ranking models, recommendation algorithms, and forecasting methods. Collaborate with data scientists, product managers and other teams to brainstorm best approaches for solving the problems at hand, be they product ...

management, options trading and trading analytics. The successful candidate will contribute to all areas of the trading platform, including risk calculations, volatility models, execution algorithms, research tooling and infrastructure. This is a highly technical role in a fast-moving environment where engineering quality, performance and latency have a direct impact ...

management, options trading and trading analytics. The successful candidate will contribute to all areas of the trading platform, including risk calculations, volatility models, execution algorithms, research tooling and infrastructure. This is a highly technical role in a fast-moving environment where engineering quality, performance and latency have a direct impact ...

have the opportunity to manage the complex challenges of scale which are unique to Google Cloud, while using your expertise in coding, algorithms, complexity analysis and large‐scale system design. SRE's culture of intellectual curiosity, problem solving and openness is key to its success. Our organization brings together people ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...

ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured ...