Microsoft jobs in london london city of united kingdom

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Create and implement comprehensive evaluation frameworks and red-teaming methodologies to assess model safety across diverse scenarios, edge cases, and potential failure modes. Build automated safety testing systems, generalize safety solutions into repeatable frameworks, and write efficient code for safety model pipelines and intervention systems. Maintain a user-oriented perspective by understanding safety needs from user perspectives, validating safety approaches through user research, and serving as a trusted advisor on AI safety matters Track advances in AI safety research, identify relevant state-of-the-art techniques, and adapt safety algorithms to drive innovation in production systems serving millions of users. Bachelor's Degree in Computer Science, or related technical discipline AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience prompting and working with large language models. Experience writing production-quality Python code. 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Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND experience in business analytics, data science, software development, data modelling or data engineering work OR equivalent experience. Expertise in large scale data engineering ideally applied to AI Expertise in Spark, Kubernetes or similar. Design and develop data pipelines that ingest enormous amounts of multi-modal training data (text, audio, images, video). Build and maintain cutting-edge infrastructure that can store and process the petabytes of data needed to power models. Partner with the pretraining and post-training teams to improve our data recipe by rigorous and careful experimentation. 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You will work in a small team within a broader organisation, designing and validating hypothesis across a cutting edge language modelling stack. Work in these roles will involve a hybrid of research and engineering, and can flex across every aspect of the language modelling stack to make models that are as useful and safe for health as possible. Deep, full-stack expertise in designing and evaluating AI applications. Evidence of this may include research papers at top AI conferences and journals, open source projects, industry experience in building production AI stacks. Strong intuition about pre/post training, metric design for AI, prompt engineering methodologies, and AI systems design. Demonstrated experience in one or more of the following areas: prompt engineering, experimental design, language model evaluations, fine tuning, reinforcement learning/direct preference optimization, data curation, and classic machine learning principles. Bachelor's Degree in Computer Science, or related technical discipline AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Demonstrated full-stack experience in large-scale AI. Empirical evidence of this in the form of top tier publications, open source contributions, and/or on-the-job work experience. Deeper expertise in one or more parts of the AI stack, including prompt engineering, pre-training, fine-tuning, reinforcement learning and direct preference optimization, data curation, LLM inference, orchestration, evaluation pipelines, and deployment. Ability to flex across research and engineering boundaries, wearing a bit of both hats. Passionate about conversational AI and its deployment. Demonstrated written and verbal communication skills with the ability to work closely with cross-functional teams, including product managers, designers, and other engineers. 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