Principal Software Engineer - AI Infrastructure

• Own the architecture and technical direction of major components of the training platform for Large Language Models (LLMs), Small Language Models (SLMs), multimodal, code-specific, and open-weight models, ensuring designs meet scalability, reliability, security, and cost-efficiency requirements.
• Partner directly with customers, including researchers and product teams across Microsoft, to understand their training and evaluation needs, translate them into platform requirements, and measure success through customer experience and outcomes.
• Drive ambiguous, multi-quarter initiatives from proposal to production, breaking work into milestones, identifying dependencies, and negotiating ownership and delivery commitments with partner teams across Cloud + AI (C+AI).
• Build on and extend open-source training frameworks (for example, PyTorch, DeepSpeed, Megatron-LM, and Hugging Face libraries), adapting them for proprietary and open-weight models and contributing improvements back to the community where appropriate.
• Design and build services that serve production traffic while meeting Microsoft security, privacy, Responsible AI, and compliance requirements, including auditable evidence of compliance.
• Lead debugging and resolution of complex issues in distributed training jobs and production services; participate in the on-call rotation as a Designated Responsible Individual (DRI), lead incident retrospectives, and improve troubleshooting guides, testing, safe deployment, and monitoring.
• Mentor engineers and raise the technical bar through design reviews, code reviews, and ownership of the team's test strategy, while championing AI-assisted engineering tools and practices across the software development lifecycle.

Required 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 equivalent experience.
   

Preferred Qualifications: 

• Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• 5+ years designing, building, and operating large-scale distributed systems or cloud platforms with end-to-end ownership of production services, including Kubernetes-based containerized platforms on Microsoft Azure, Amazon Web Services (AWS), or Google Cloud, control-plane or backend services that manage compute, storage, and networking, and secure multi-tenant designs covering authentication, authorization, workload identity, quota management, and isolation.
• 3+ years building machine learning training infrastructure, such as Graphics Processing Unit (GPU) cluster scheduling, data pipelines, and checkpointing and storage systems, using open-source training frameworks such as PyTorch Fully Sharded Data Parallel (FSDP), DeepSpeed, Megatron-LM, Hugging Face Transformers and Accelerate, or Ray Train; contributions to open-source projects are a strong plus.
• 2+ years training, fine-tuning, or evaluating open-weight models (for example, Llama, Mistral, Phi, Qwen, or DeepSeek model families) at multi-node scale.
• 2+ years building or operating Reinforcement Learning (RL) post-training pipelines for Large Language Models (LLMs) and agentic models using verl, slime, MILES, or comparable frameworks, with a demonstrated understanding of the end-to-end training stack, including pretraining and post-training frameworks, Reinforcement Learning Environments (RLEs), rollout inference engines (for example, vLLM or SGLang), evaluation, and checkpointing, and how design choices at each layer affect throughput, stability, and cost.
• 3+ years serving as an on-call or Designated Responsible Individual (DRI) engineer for production services operating against defined availability or latency targets, including incident response, postmortems, structured logging, distributed tracing, dashboards, alerting, automated validation, and incident playbooks.
• 2+ years partnering directly with internal or external customers to define requirements, instrument customer experience (for example, adoption, satisfaction, or time-to-first-result metrics), and prioritize engineering work by customer impact.
• Experience leading technical design across 2+ teams, driving ambiguous multi-quarter initiatives to completion, and mentoring senior engineers.

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.