Senior Research Engineer

Microsoft•Redmond, WA

About The Position

As a Senior Research Engineer at Microsoft, you will help advance Microsoft’s mission to empower every person and every organization on the planet to achieve more by building intelligent, scalable cloud services that power Dynamics 365 Contact Center. This role sits at the intersection of AI, software engineering, and enterprise customer engagement. You will contribute to the design and delivery of AI-first capabilities that enable organizations to connect with, understand, and serve their customers across digital and voice channels. Mission and Impact Dynamics 365 Contact Center is a core part of Microsoft’s customer experience platform, enabling enterprises to deliver intelligent, always-on, omnichannel customer support powered by AI and cloud scale. 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.

Requirements

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.
Proficiency in Python and at least one deep learning framework such as PyTorch, JAX, or TensorFlow.
Experience deploying Fine Tuned LLMs or multimodal models in live production environments.
Experience shipping and maintaining production AI systems.
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.

Nice To Haves

Master’s degree and 3 or more years in applied ML or AI research and product engineering, OR PhD in a relevant field and 2 or more years 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.

Responsibilities

Build AI-First Contact Center Experiences
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
Partner with product teams to improve customer and agent outcomes
End-to-End System Development
Own features end-to-end from design to live-site operations
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 Engineering
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.
Code level (implementation): Turn ideas into robust, tested, maintainable modules; integrate with CI/CD; profile and optimize for latency and throughput.
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