AI Jobs in London

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As an Automotive and Robotics SoC Architect, you will define scalable, top-down system architectures that unify CPU and AI technologies for next-generation automotive applications. This role involves shaping the architectural direction of the automotive and robotics portfolio to ensure products meet the industry's high standards for performance, safety, reliability, and security. The position requires strong technical leadership, systems thinking, and cross-functional collaboration to deliver world-class automotive solutions.

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The Backend Software Engineer on the Engine Team (Voice Agent) at Deepgram will lead the design and implementation of the Voice Agent product, including designing and implementing secure, robust, and scalable services for speech processing. Responsibilities include building integrations to support telephony providers, RAG systems, and diverse deployment scenarios; engineering services for testability and observability within a complex chain of AI models; improving core inference services in networking, speech processing, model orchestration, and observability; developing integrations with in-house, third-party, and open-source AI models for perception and conversational dynamics; debugging complex system issues involving networking, scheduling, and highly concurrent workloads; rapidly customizing backend services to support customer needs; and partnering with Product to design and implement new services, features, and products end to end.

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The job requires industry experience as a research engineer in an AI-related company and involves working, learning, and teaching within a collaborative team focused on solving challenging problems.

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As a Senior Data Scientist at Faculty, you will be responsible for designing and building AI-powered computational twins tailored for each unique Frontier deployment. You will lead data science efforts within cross-functional teams, collaborating with engineers, designers, and commercial leads to ensure project success. Your role involves deeply understanding core customer challenges to deliver significant real-world value, conducting rigorous exploratory data analysis, model building, validation, and performance monitoring. Additionally, you will support client relationships by partnering with the commercial team to shape strategic project direction and mentor other data scientists through task leadership and potential line management.

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Build and maintain autonomous agent-based solutions that generate real impact on internal teams to better serve customers. Design, architect, and deliver AI solutions by partnering with technical and business teams. Own the end-to-end lifecycle from design through experimentation, deployment, user adoption, impact measurement, and continuous iteration. Mentor and scale expertise by coaching engineers, setting direction for best practices, and acting as a technical sounding board. Collaborate cross-functionally to ensure solutions meet user needs, generate company-wide impact, and align with other Engineering squads.

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Design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; apply structured scoring criteria to assess multi-step problem solving.

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Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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Design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; apply structured scoring criteria to assess multi-step problem solving.

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Contributors design graduate- and industry-level automotive engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.

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The AI Deployment Engineer Manager, Digital Natives, is responsible for owning the strategy and operating model of the Digital Native ADE team across EMEA, aligning with company objectives and customer needs, triaging incoming opportunities, and managing the Digital Native portfolio. The role includes leading, building, and mentoring a team of ADEs to deliver exceptional customer outcomes, demonstrated by production customer applications and increased API adoption. The manager acts as the technical advocate for customers, synthesizing their needs to influence Research and Applied Product/Engineering roadmaps, serving as the primary technical escalation point during development, maintaining direct communication with executive-level stakeholders, and serving as an industry thought leader promoting the safe and innovative application of the technology across sectors.