Senior Research Engineer at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

234700.0

Posted On

18 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Initiatives, Foundation Models, Multi-agent Systems, Copilot Experiences, Scalable Systems, State of the Art, MLOps, PyTorch, TensorFlow, CI/CD, RAG, LLMs, Responsible AI, Experimentation, Data Curation, System Optimization

Industry

Software Development

Description

Overview Role Summary As a Research Engineer at Microsoft, you will set the technical vision and lead transformative AI initiatives that shape the future of Microsoft’s products and services. Operating at the intersection of advanced research, engineering, and product strategy, you will drive innovation at scale, architecting solutions that deliver real-world impact for millions of users. You will be a recognised technical leader, influencing cross-organisational strategy, mentoring senior engineers, and representing Microsoft in the global research community. Mission & impact: We are in an era of unprecedented AI innovation. As Microsoft leads the way in foundation models, multimodal systems, and AI agents, our goal is to build an open architecture platform where users can interact with tailored AI agents that drive tangible, real-world outcomes. As a Research Engineer, you will: Define and execute technical strategy for foundational models, multi-agent systems, and next-generation Copilot experiences, especially within Business & Industry Copilot. Lead cross-team efforts to deliver scalable, reliable, and responsible AI systems. Advance the state of the art and translate breakthroughs into measurable customer and business impact. 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. Responsibilities Bringing State-of-the-Art Research to Products Architect and deliver complex AI systems across model development, data, infra, evaluation, and deployment spanning multiple product lines. Set technical direction for large programs; drive alignment across Research, Engineering, and Product. Build and harden prototypes into production-ready services using robust software engineering and MLOps practices. Integrate LLMs, multimodal models, multi-agent architectures, and RAG into Microsoft’s ecosystem. Establish best practices for MLOps, governance, and Responsible AI, compliant with Microsoft principles and industry standards. Drive original research and thought leadership (whitepapers, internal notes, patents); convert insights into shipped capabilities. Research Translation: Continuously review emerging work; identify high-potential methods and adapt them to Microsoft problem spaces. Production Integration: Turn research prototypes into production-quality code optimized for scale, latency, and maintainability. End-to-End System Development ML Design & Architecture: Own end-to-end pipeline from data prep, training, evaluation, deployment, and feedback loops. Identify and resolve model quality gaps, latency issues, and scale bottlenecks using PyTorch, or TensorFlow. Operate CI/CD and MLOps workflows including model versioning, retraining, evaluation, and monitoring. Integrate AI components into Microsoft products in close partnership with engineering and product teams. Data-Driven Innovation Evaluation & Instrumentation: Build robust offline/online evals, experimentation frameworks, and telemetry for model/system performance. Learning Loop Creation: Operationalize continuous learning from user feedback and system signals; close the loop from experimentation to deployment. Experimentation & E2E Validation: Design controlled experiments, analyze results, and drive product/model decisions with data. Develop proofs of concept that validate ideas quickly at realistic scales. Curate high-signal datasets, including synthetic and red-team corpora, and establish labeling protocols and data quality checks tied to evaluation KPIs. Cross-Functional Collaboration Partner with software engineers, scientists, designers, and product managers to deliver high-impact AI features. Translate research breakthroughs into scalable applications aligned with product priorities. Communicate findings and decisions through internal forums, demos, and documentation. Responsible AI & Ethics Identify and mitigate risks related to fairness, privacy, safety, security, hallucination, and data leakage. Uphold Microsoft’s Responsible AI principles throughout the lifecycle. Contribute to internal policies, auditing practices, and tools for responsible AI. Operating Altitudes Paper level (ideas and math): Read, critique, and adapt the latest research; identify gaps; design methods with clear trade-offs and guarantees; communicate complex ideas clearly. Example: “This objective is brittle under our data regime. Here is a tighter analysis and a revised loss we can test this sprint.” Code level (implementation): Turn ideas into robust, tested, maintainable modules; integrate with CI/CD; profile and optimize for latency and throughput. Example: “Refactored the prototype into a reusable PyTorch component, added unit tests and benchmarks, and cut P95 inference latency by 30%.” Specialty Technical Areas Large-scale training and fine-tuning of LLMs, vision-language, or multimodal models. Multi-agent systems, dialogue agents, and copilots. Optimization of inference speed, accuracy, reliability, and cost in production. Retrieval systems and hybrid architectures using RAG and vector databases. ML for real-world data constraints such as missing data, noisy labels, and class imbalance. Qualifications Required Qualifications: Bachelor'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. Proven track record leading large-scale AI systems and cross-org initiatives that shipped. Solid software engineering foundations and hands-on depth in Python plus deep-learning frameworks (PyTorch/ TensorFlow) and modern MLOps/tooling. Experience shipping and maintaining production AI systems. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field and 1 or more years in applied ML or AI research and product engineering, OR 1 or more years experiece with generative AI, LLMs, or related ML algorithms. Experience with Microsoft’s LLMOps stack: Azure AI Foundry, Azure Machine Learning, Semantic Kernel, Azure OpenAI Service, and Azure AI Search for vector/RAG. Familiarity with responsible AI evaluation frameworks and bias mitigation methods. Experience across the product lifecycle from ideation to shipping. #BICJOBS #CXAjobs #CESjobs 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 $158,400 - $258,000 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 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.

Responsibilities

This role involves defining and executing technical strategy for foundational models and multi-agent systems, leading cross-team efforts to deliver scalable and responsible AI systems, and advancing the state of the art to achieve measurable customer impact. Responsibilities include architecting and delivering complex AI systems across the entire pipeline from model development to deployment, and establishing best practices for MLOps and Responsible AI.