Develop and iterate post-training methods for large language models: drive data collection, build evaluations, design experiments, and apply reward modeling and RL techniques to improve model capabilities and deployment.
Overview
Responsibilities
Qualifications
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 and improving our pre-trained models to advance the state-of-the-art on a wide variety of internal and external benchmarks. Our goal is to push our models’ capabilities in reasoning and instruction following, math, code, and tool use and agentic tasks, among many other areas.
This role involves contributions to all stages of the post-training process: driving data collection and acquisition, building evaluations of model capabilities, and applying advanced reward modeling and RL techniques to develop and improve the post-training recipe. We work on the bleeding edge and leverage the most powerful pretrained models and algorithms for our needs. We are an interdisciplinary team of engineers and scientists, learning from each other and collaborating to create the best models.
We are looking for outstanding individuals excited about contributing to the next generation of models that will transform the field. We are looking for candidates who:
- Are passionate about advancing the state of post-training research;
- Have experience with reward modeling, RL, or other post-training techniques;
- Will thrive in a highly collaborative, fast-paced environment;
- Have a high degree of craftsmanship and pay close attention to details, consistently striving for engineering excellence.;
- Are willing to contribute meaningfully as individuals and take end-to-end ownership of projects
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction.
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. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models!
Responsibilities
- Develop data collection, evaluation, and post-training methods for models.
- Design hypotheses and experiment plans for rapidly iterating on model performance.
Qualifications
Required Qualifications
- Bachelor's Degree in Computer Science, Machine Learning, Mathematics, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
- OR equivalent experience.
- Have experience with reward modeling, RL, or other post-training techniques.
Preferred Qualifications
- Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
- OR Master's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
- OR equivalent experience.
- 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.
Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
Similar Jobs
Software • Quantum Computing • Metaverse • Infrastructure as a Service (IaaS)
Build and optimize distributed systems supporting large-scale reinforcement learning, LLM post-training, inference, and production AI workloads. Develop reliable platform services, improve researcher iteration loops, debug model-hardware interactions, optimize performance, and enhance observability, security, scalability, and operational reliability. Participate in architecture, coding, incident response, deployment planning, and on-call support. Required qualifications include a technical bachelor’s degree or equivalent experience and at least two years of software engineering experience.
Top Skills:
CC#C++Cloud ComputingContainersDisaggregated InferenceDistributed Training InfrastructureGenerative AiGpu ComputingJavaLarge Language ModelsPost-TrainingPythonRayReinforcement LearningSglangSlimeVerlVllm
Artificial Intelligence • Machine Learning • Software
Design, optimize, and deploy large language model training and post-training pipelines. Improve model quality through fine-tuning, reinforcement learning, preference optimization, evaluation, and experimentation. Build PyTorch-based infrastructure, optimize distributed multi-GPU training, diagnose performance and convergence issues, and develop production-ready AI systems. Collaborate with researchers, infrastructure engineers, platform teams, and customers while contributing to open-source projects and reusable training capabilities.
Top Skills:
CudaDeepspeedDistributed TrainingDpoFsdpGpu Performance OptimizationGrpoHugging Face TransformersLightning FabricMegatron-LmMixed PrecisionMulti-Gpu SystemsNvidia MoltPeftPpoPythonPyTorchReinforcement LearningReward ModelingRlhfSglangSupervised Fine-TuningTensorrtTransformer-Based Language ModelsTritonTrlVllm
Artificial Intelligence • Information Technology • Software
Build an end-to-end decentralized RL post-training stack for large language models, including rollout ingestion, reward computation, policy updates, weight synchronization, and evaluation. Develop algorithms for asynchronous, high-latency, partially trusted environments and ship post-trained models as public artifacts. The role requires hands-on RLHF, RLVR, or reasoning-focused RL experience, strong Python and PyTorch engineering, and research capability. Experience with slow networks, decentralized systems, model serving, reward modeling, or P2P networking is advantageous.
Top Skills:
Nat TraversalP2P NetworkingPythonPyTorchSglangVllm
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
