Overview

As a Senior Research Engineer at Microsoft, you will advance Microsoft’s mission to empower every person and every organization to achieve more. You will help build and integrate cutting-edge AI into Microsoft products and services within the Business & Industry Copilot (BIC) group, ensuring solutions are inclusive, ethical, and impactful. This role blends applied research, machine learning engineering, and product innovation. You will lead efforts to ship reliable, production-grade AI systems across the stack, from model development and fine-tuning to performance optimization and deployment.

Mission and 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:

• Bridge the gap between state-of-the-art research and customer-facing features.
• Drive systems-level innovation across models, infrastructure, and deployment.
• Champion responsible AI by embedding fairness, safety, privacy, and performance from the ground up.

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:

• Design and implement AI systems using foundation models, prompt engineering, retrieval-augmented generation, multi-agent architectures, and classic ML.
• Fine-tune large language models on domain-specific data and evaluate via offline and online methods such as A/B testing, telemetry, and shadow deployments.
• Build and harden prototypes into production-ready services using robust software engineering and MLOps practices.
• 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.

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 Py Torch, or Tensor Flow.
• 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 Py Torch 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 PythonOR equivalent experience.
• Proficiency in Python and at least one deep learning framework such as Py Torch, JAX, or Tensor Flow.
• Experience deploying Fine Tuned LLMs or multimodal models in live production environments.
• 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.