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Top Machine Learning Jobs in London
As an ML Engineer, you will develop and implement features for revenue management applications, utilizing data analysis and machine learning models to optimize pricing strategies for hospitality customers. Collaboration with product and engineering teams is crucial to identify improvements and drive revenue growth.
As a ML Engineer at Cloudbeds, you will develop end-to-end features to optimize pricing strategies for customers. This includes deploying machine learning models, analyzing data trends, collaborating with cross-functional teams, and communicating insights to stakeholders while driving revenue growth for hotels.
As a ML Engineer at Cloudbeds, you will develop end-to-end features to optimize revenue through informed pricing strategies. Your role involves building machine learning models, data pipelines, analyzing data, and collaborating with cross-functional teams for product improvement.
As a ML Engineer at Cloudbeds, you will develop end-to-end features for revenue management in the hospitality sector, utilizing machine learning models and data analysis to identify trends and improve pricing strategies. You'll collaborate with teams to enhance product offerings and communicate insights to stakeholders.
As a Senior ML Platform Engineer, you'll enhance the ML platform to support research teams, managing cloud infrastructure and tooling for data collection and model training. You will ensure seamless operations for researchers by maintaining and improving the infrastructure for GPU clusters in a cloud environment.
The Senior ML Engineer will own the components of the Machine Learning pipeline, involving research in Deep Learning, processing multi-modal data, and training models. Responsibilities include developing and deploying models, enhancing accuracy, mentoring engineers, and managing projects from spec to deployment.
As a Data & AI/ML Architect, you will conduct workshops, lead teams for project delivery, create solution architectures, support continuous learning, and mentor junior engineers while managing complex technical designs for GCP customers.
As a Software (ML Product) Engineer at iterative.ai, you will enhance user workflows for DVC, collaborate with technical product managers, and improve MLOps practices within ML teams. Responsibilities include optimizing the tool’s usability and driving projects like dvclive, emphasizing software engineering skills and communication.
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The Senior Machine Learning Engineer will lead the design and implementation of advanced machine learning models focusing on NLP and LLM technologies. Responsibilities include developing efficient data processing pipelines, performing large-scale data analysis, and collaborating with teams to optimize workflows in the education and research sector.
As a Senior Software Engineer in Test (Graphics/ML) at PlayStation, you will support development work in AI/ML for Graphics, creating scripts and tools to enhance testing efficiency. You'll work on code quality, continuous integration, and bespoke test frameworks, collaborating globally while learning about PlayStation systems and technologies.
The ML Platform Engineer will design and maintain scalable machine learning platforms, automate deployment and scaling of ML infrastructure, manage model lifecycles, and optimize performance. Collaboration with data scientists and engineers is essential for effective model development, alongside ensuring security, compliance, and efficient tooling. The role emphasizes adaptive problem-solving and a strong focus on product-driven solutions.
The Staff Engineer in Machine Learning will provide technical leadership across the AML team, focusing on delivering AI/ML solutions, defining systems architecture, and driving innovation. Responsibilities include overseeing high-impact projects, ensuring best practices in engineering and collaborating with multiple business units to align deliverables with business goals.
Join the Redis AI team to research, design, and implement advancements in Large Language Models (LLMs), optimizing for relevance and latency. Tackle complex NLP problems, enhance tools for model orchestration, and implement algorithms for efficiency at scale.
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