We're looking for data scientists to help build the next generation of post-training methods for frontier models at Microsoft AI. You'll join a small, high-impact team working across all stages of post-training, with a focus on evaluation design, high-quality training data, and scalable data pipelines for state-of-the-art foundation models.
\nIn this role, you'll help turn raw model capability into reliable, aligned, and measurable performance improvements, directly shaping how frontier models behave in real-world deployments.
\nAbout the Role:
\nMicrosoft AI is building the next generation of frontier models that power Copilot and other large-scale AI experiences. The Post-Training team is responsible for transforming powerful pretrained models into robust, aligned, and high-performing systems used by millions of people worldwide.
\nOur work focuses on improving general quality, instruction following, coding and math ability, tool use, agentic behaviors, personality, and other critical model capabilities. We operate across the full post-training lifecycle , from data generation and curation, to evaluation and diagnostics, to reward modeling and reinforcement learning.
\nWe are a small, highly autonomous team that works closely with pre-training, product, and engineering partners to rapidly iterate on ideas, run large-scale experiments, and safely advance model capabilities. Each team member owns meaningful parts of the post-training pipeline and has direct access to the compute, data, and decision-making needed to move quickly from insight to production.
\nMicrosoft Superintelligence Team
\nThis role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence,ultra-capable systems that remain controllable, safety-aligned, and anchored to human values.
\nOur mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society,advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact.
\nResponsibilities
\nDesign evaluations of advanced model capabilities and use them to drive rapid, high-signal iteration loops Work with vendors to produce high quality evaluation and training data Build data pipelines to produce high quality evaluation and training data Build data flywheels to hill-climb on model weaknesses, using data from various surfaces where our models are deployed Ensure optimal quality, quantity and coverage of data across our post-training stages Run post-training experiments and ablations to produce models that climb our evals Embody our culture and values.
\nWe’re Looking For People Who:
\nHave deep experience with LLMs, either training them or applying them in production Have developed production-scale data pipelines for synthesizing, curating, or processing large quantities of data Can design, run, and interpret large-scale ML experiments with careful statistical and empirical reasoning.
\nPossess strong generalist engineering and mathematical skills.
\nHave clear written and verbal communication, and the ability to collaborate effectively with researchers, engineers and other disciplines.
\nBonus skills:
\nDemonstrated SOTA results in any area of large-scale training, inference, or evaluation.
\nXML job scraping automation by YubHub
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