This role focuses on developing and improving LLM models for general-purpose capabilities and products, with a strong emphasis on agentive experiences. Responsibilities include data acquisition/generation, generalizing ML solutions, leading evaluation efforts, adapting state-of-the-art research, and writing production-quality code. The role involves fine-tuning, training classifiers, and engineering prompts, aiming to push the boundaries of AI towards controllable, safety-aligned superintelligence.

Seeking Senior/Principal Applied Scientists to fine-tune LLMs on tenant data for M365 Copilot, creating task-specific agents and solutions. Role involves writing training pipelines, designing experiments, implementing inference solutions, and shipping models to customers. Focus on advancing LLM capabilities in an enterprise context.

The role focuses on building and scaling Responsible AI service components, specifically involving supervised fine-tuning of LLMs with RLHF, conducting evaluations, and developing agent adversarial evaluations and safety mitigations. The goal is to enable customers to use AI responsibly and securely.

Principal Machine Learning Engineer for Health Futures team focused on accelerating training of generative models, advancing model capabilities, and optimizing training/evaluation/inference pipelines for health and life sciences applications.

This role focuses on the post-training of large language models (LLMs) to improve their capabilities in areas like reasoning, instruction following, math, code, and tool use. The responsibilities include data collection, building evaluations, and applying advanced reward modeling and RL techniques. The goal is to advance the state-of-the-art in LLM performance and contribute to the development of superintelligent AI systems.

Data Scientist role focused on applying machine learning and statistical analysis to develop, train, optimize, and evaluate models for enterprise customers. The role involves data preparation, model evaluation, and integrating these models into customer systems, with a strong emphasis on Responsible AI.

This role focuses on developing and operationalizing machine learning models for threat detection within Microsoft's security ecosystem. It involves building supervised and unsupervised models, applying graph-focused ML techniques, and analyzing large security datasets. The role also includes data engineering for feature pipelines and running experiments to improve detection quality, with a strong emphasis on translating research into production-ready solutions.

This role is for a Principal Software Engineering Manager within Microsoft's AI Platform organization, specifically on the AI Foundry OSS Model Customization Team. The team focuses on enabling data scientists and developers to build, train, deploy, and manage machine learning models, with a specialization in customizing open-source models. The manager will lead and develop a team of engineers, focusing on scalable services, customer needs, and career development, while collaborating with external partners and internal Microsoft teams. A strong background in Generative AI, ML, deep learning, NLP, transformer models, and cloud platforms is required.