1 day ago
Base Salary
$143k - $304k/yr
Responsibilities
- Own the architecture and technical direction of major components of the AI training platform for LLMs, SLMs, multimodal, code-specific, and open-weight models.
- Partner with researchers, product teams, and other internal customers to define training and evaluation requirements and measure customer outcomes.
- Drive ambiguous multi-quarter initiatives from proposal through production, including milestones, dependencies, ownership, and delivery commitments.
- Build and extend open-source training frameworks and contribute improvements to the community where appropriate.
- Design and build secure, privacy-conscious, compliant production services with auditable compliance evidence.
- Debug complex distributed training and production-service issues, participate in the on-call rotation as a DRI, lead incident retrospectives, and improve troubleshooting, testing, deployment, and monitoring.
- Mentor engineers and raise the technical bar through design reviews, code reviews, test-strategy ownership, and adoption of AI-assisted engineering practices.
Requirements
- Bachelor’s degree in Computer Science or a related technical field and 6+ years of technical engineering experience with coding in C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
- Preferred qualifications include a master’s degree and 8+ years of experience, or a bachelor’s degree and 12+ years of experience, or equivalent experience.
- 5+ years designing, building, and operating large-scale distributed systems or cloud platforms with end-to-end production-service ownership.
- 3+ years building machine learning training infrastructure, including GPU cluster scheduling, data pipelines, and checkpointing and storage systems.
- 2+ years training, fine-tuning, or evaluating open-weight models at multi-node scale.
- 2+ years building or operating reinforcement learning post-training pipelines for LLMs and agentic models, including understanding of pretraining, post-training, rollout inference, evaluation, and checkpointing.
- 3+ years serving as an on-call or DRI engineer for production services with availability or latency targets, incident response, postmortems, logging, tracing, dashboards, alerting, automated validation, and playbooks.
- 2+ years partnering directly with internal or external customers to define requirements, measure customer experience, and prioritize work by customer impact.
- Experience leading technical design across at least two teams, completing ambiguous multi-quarter initiatives, and mentoring senior engineers.
- Experience with Kubernetes-based containerized platforms on Azure, AWS, or Google Cloud and secure multi-tenant systems is preferred.
- Experience with PyTorch FSDP, DeepSpeed, Megatron-LM, Hugging Face Transformers, Accelerate, Ray Train, verl, slime, MILES, vLLM, or SGLang is preferred.
Benefits
- The typical U.S. base pay range is USD $142,800–$274,800 per year, with a separate USD $188,000–$304,200 annual range for specified San Francisco Bay Area and New York City metropolitan area locations.
- Certain roles may be eligible for benefits and other compensation.
- The position will remain open for a minimum of five days and applications are accepted on an ongoing basis until filled.
Tech Stack
AWSAzureCC#C++Google CloudGoogle Cloud PlatformHugging Face TransformersJavaJavaScriptKubernetesPythonPyTorch
Categories
About Microsoft
Microsoft develops operating systems, productivity software, cloud services, developer tools, and consumer devices for individuals, enterprises, and governments. Its main products include Windows, Microsoft 365, Azure, Visual Studio/GitHub, Xbox, and LinkedIn; revenue comes from software subscriptions and licenses, cloud consumption, hardware sales, and advertising. Founded in 1975 and headquartered in Redmond, Washington, Microsoft is a public company traded on Nasdaq.