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At Microsoft AI, we are on a mission to develop the most cutting-edge algorithms for post-training large language models (LLMs) and ship those models to millions of users using Copilot every day. The AI Post-Training team at Microsoft AI is responsible for all aspects of post-training the models that we serve in Copilot, including: data collection, building evaluations that are aligned with our product and model capability goals, prototyping new capabilities to make Copilot more powerful, developing new finetuning algorithms to supercharge our models, working with platform and engineering teams to deploy those models, and closing the loop by improving the models with feedback we receive from our users.
Job Responsibility:
Develop data collection, evaluation, and finetuning methods for models
Design hypotheses and experiment plans for rapidly iterating on model performance
Prototype new model features and capabilities and collaborate with engineers and researchers across Microsoft AI to make them a reality
Collaborate with pretraining and product platform teams to establish good vertical integration and ship models that Copilot users love
Embody our culture and values
Requirements:
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
Expertise in post-training of AI models
Demonstrated experience in large-scale AI
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
Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in AI
Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team
Proven research track record in a domain related field supported by exceptional papers