Jobs via Dice ยท 4 days ago
Senior AI Applied Scientist
Redmond, WA
Full-time
Onsite
Mid, Senior Level
$120K/yr - $235K/yr
4+ years expMicrosoft is a leading technology company empowering individuals and organizations through innovative solutions. The Senior AI Applied Scientist will advance AI technologies for Dynamics 365 Contact Center, collaborating across teams to develop and integrate multimodal AI solutions that enhance customer experience.
Computer Software
Responsibilities
Build collaborative relationships with product and business groups to deliver AI-driven impact
Light weight Fine-tuning (LoRA etc.) of multimodal/MLLM models (Voice assistants etc.) to improve Agent name-entity recognition, Agent Reasoning and Agent Response quality
Multimodal data generation, Voice assistant model evaluations - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis
Implement state-of-the-art using foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques
Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, support MLOps/AIOps
Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts
Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact
Share insights on industry trends and applied technologies with engineering and product teams
Formulate strategic plans that integrate state-of-the-art research to meet business goals
Maintain clear documentation of experiments, results, and methodologies
Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing
Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring
Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring
Design, develop, and integrate generative AI solutions using foundation models and more
Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classical ML, and optimization techniques to adapt out-of-the-box solutions to business problems
Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps
Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics
Address scalability and performance issues using large-scale computing frameworks
Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams
Qualification
Required
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) + OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) + OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) + OR equivalent experience
3+ year(s) Experience in developing and deploying live production systems
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
Preferred
Building RAGs and fine-tuning speech language models Whisper, wav2vec2 etc
Understanding of SLU, ASR, TTS, NLP, NLU domain, Fine-tuning techniques
Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow)
Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines
2+ years of experience publishing in peer-reviewed venues or filing patents
Experience presenting at conferences or industry events
2+ years of experience conducting research in academic or industry settings
Experience in working with Generative AI models and ML stacks
Experience across the product lifecycle from ideation to shipping
Company
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Funding
Current Stage
Early StageCompany data provided by crunchbase