In 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.

About the Role:

Microsoft 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.

Our 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.

We 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.

Microsoft Superintelligence Team

This 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.

Our 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.

Responsibilities

Design 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.

We’re Looking For People Who:

Have 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.

Possess strong generalist engineering and mathematical skills.

Have clear written and verbal communication, and the ability to collaborate effectively with researchers, engineers and other disciplines.

Bonus skills:

Demonstrated SOTA results in any area of large-scale training, inference, or evaluation.

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