2 months ago
Base Salary
$143k - $304k/yr
Responsibilities
- Design, build, and operationalize scalable machine learning and deep learning models using containers and orchestration platforms.
- Develop LLM prompt and fine-tuning strategies, evaluation pipelines, and model quality, latency, and cost optimizations.
- Architect, implement, and operate highly available, fault-tolerant, low-latency distributed services at hyperscale.
- Build model-serving and inference infrastructure, including caching, batching, GPU capacity management, and A/B experimentation.
- Design scalable APIs, data pipelines, and feature or signal stores for reliable data flow between ML systems and product surfaces.
- Drive monitoring, instrumentation, capacity planning, and incident response for ML-backed services.
- Collaborate with applied scientists, data scientists, backend engineers, and product teams to deliver production ML systems.
- Participate in code reviews and architecture discussions while mentoring engineers across ML and systems disciplines.
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.
- Master’s degree with 8+ years of experience or a bachelor’s degree with 12+ years of experience is preferred, or equivalent experience.
- Experience designing, developing, and operating multi-tiered distributed services at scale.
- Hands-on experience building, deploying, and operating production machine learning systems, including model training, evaluation, or LLM-based applications.
- Experience across full product cycles from initial design through final delivery.
- Experience with prompt engineering, retrieval-augmented generation, fine-tuning, and model evaluation frameworks.
- Experience with distributed training, inference optimization, GPU capacity management, and orchestration platforms such as Kubernetes.
- Experience with large-scale data systems, streaming, caching such as Redis, feature stores, and experimentation platforms.
- Ability and interest in working across the machine learning and distributed systems boundary.
Benefits
- Base pay is listed at USD $142,800–$274,800 annually across the U.S., with a separate USD $188,000–$304,200 annual range for specified San Francisco Bay Area and New York City metropolitan locations.
- Certain roles may be eligible for benefits and other compensation.
- Employees within the stated commuting distances of designated offices are expected to work from the office at least four days per week starting January 26, 2026, subject to local law and jurisdiction.
- The position will remain open for at least five days and accepts applications on an ongoing basis until filled.
Tech Stack
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.