AI Jobs in London

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The Lead Machine Learning Engineer will set the technical direction for complex ML projects, balancing trade-offs and guiding team priorities. Responsibilities include designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions. The role involves defining project problems, developing roadmaps, overseeing delivery across multiple work-streams in ill-defined, high-risk environments, and driving the development of shared resources and libraries across the organisation. The engineer will guide other engineers in contributing to these resources, lead hiring processes, make informed selection decisions, mentor multiple individuals to foster team growth, and develop and execute recommendations for adopting new technologies and changing working methods. Additionally, acting as a technical expert and coach for customers, accurately estimating large work-streams, and defending rationale to stakeholders is required.

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The Machine Learning Engineer is responsible for building and deploying production-grade machine learning software, tools, and infrastructure. They create reusable, scalable solutions that accelerate the delivery of ML systems. They collaborate with engineers, data scientists, and commercial leads to solve critical client challenges. They lead technical scoping and architectural decisions to ensure project feasibility and impact. They define and implement Faculty's standards for deploying machine learning at scale. Additionally, they act as technical advisors to customers and partners, translating complex ML concepts for stakeholders.

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Drive the end-to-end technical deployment of Latent Labs models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure. Design and build production-grade API integrations, data pipelines and model-serving infrastructure tailored to each customer’s requirements. Work on-site or embedded with pharma and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions. Ensure deployments meet enterprise standards for security, performance and reliability. Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including spending time working on-site at international partner locations as needed. Gather and synthesise customer feedback, translating it into actionable insights for the product, research and platform teams. Collaborate with internal teams to shape the product roadmap based on real-world deployment learnings. Create technical documentation, integration guides and best-practice resources for customers. Stay on top of the latest developments in ML infrastructure, model serving and cloud-native tooling. Gain a strong working understanding of protein and cell biology as it relates to the product. Participate in knowledge sharing, e.g., organise and present at internal reading groups.

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Develop, deploy and adapt generative models for customer environments by gaining a deep understanding of the model architectures, training data, capabilities and limitations. Collaborate with research scientists, engineers and protein designers in a joint codebase while maintaining high code standards. Drive the end-to-end technical deployment of models into customer environments, including designing production-grade API integrations and model-serving infrastructure. Adapt and fine-tune models to meet specific customer requirements and collaborate closely with research teams to ensure scientific rigour. Build machine learning data pipelines for customer-specific inference, evaluation, and feedback workflows. Ensure deployments meet customer standards for security, performance, and reliability. Work embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues, and deliver solutions, serving as the technical point of contact for assigned customers. Collaborate with customer biology teams to plan and carry out model inference against biological targets and rapidly incorporate insights back into models. Gather and synthesize customer feedback, producing actionable insights for the product, research, and platform teams. Create technical documentation, integration guides, and best-practice resources. Engage in international partner site visits when needed. Stay current with developments in machine learning, model serving, and cloud-native tooling. Gain understanding of protein and cell biology. Participate in knowledge sharing by organizing and presenting at internal reading groups and attend and present at conferences.

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As a Senior MLOps Engineer, the responsibilities include leading technical scoping and architectural decisions for high-impact ML systems, designing, building, and deploying production-grade ML software, tools, and scalable infrastructure, and defining and implementing best practices and standards for deploying machine learning at scale across the business. The role also involves collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities, acting as a trusted technical advisor to customers and partners by translating complex concepts into actionable strategies, and mentoring and developing junior engineers while actively shaping the team's engineering culture and technical depth.

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As a Senior Python Engineer, the role involves leading the development and deployment of advanced AI systems for diverse clients, designing, building, and deploying scalable, production-grade machine learning software and infrastructure that adhere to strict operational and ethical standards. Responsibilities include leading technical scoping and architectural decisions for high-impact machine learning systems, defining and implementing best practices and standards for deploying machine learning at scale, collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges, acting as a trusted technical advisor to clients by translating complex concepts into actionable strategies, and mentoring junior engineers while contributing to the team's engineering culture and technical depth.

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Building and deploying production-grade ML software, tools, and infrastructure; creating reusable, scalable solutions to accelerate the delivery of ML systems; collaborating with engineers, data scientists, and commercial leads to solve critical client challenges; leading technical scoping and architectural decisions to ensure project feasibility and impact; defining and implementing Faculty’s standards for deploying machine learning at scale; acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.

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The Platform Engineer is responsible for building robust, secure, and scalable cloud infrastructure for AI and machine learning workflows. This includes partnering with technical and non-technical stakeholders from idea generation through implementation and shipping, enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repositories, proactively identifying and recommending new ways customers can leverage cloud infrastructure to solve their challenges, creating and maintaining reusable, company-wide libraries and infrastructure-as-code, and researching and integrating the best open-source technologies to enhance Faculty's infrastructure capabilities.

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The Infrastructure Engineer is responsible for designing, building, and deploying robust, secure, and scalable cloud infrastructure for AI and machine learning workflows. They will work in a cross-functional team and partner with technical and non-technical stakeholders from the initial idea generation through to implementation and shipping. The role involves enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repositories. The engineer will proactively identify and recommend new ways customers can leverage cloud infrastructure to address their key challenges, create and maintain reusable company-wide libraries and infrastructure-as-code, and research and integrate the best open-source technologies to enhance Faculty's infrastructure capabilities.

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As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.