layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Principal Applied Scientist at Microsoft, Redmond, WA

Advance Applied AI Research to Improve Quality and Reliability
Apply deep expertise in Generative AI, deep learning, NLP, and multimodal models to translate cutting-edge research into high-impact, production-ready AI solutions.
Design and execute experiments that measurably improve agent planning, memory, grounding, reasoning, and long‑horizon task completion.
Perform lightweight fine‑tuning
(e.g., LoRA and related techniques) on multimodal and large multimodal language models—to improve entity recognition, reasoning accuracy, and response quality.
Implement state‑of‑the‑art approaches using foundation models, advanced prompt engineering, RAG, knowledge graphs, and multi‑agent architectures
, complemented by classical ML techniques where appropriate.
Build and Ship Scalable Agentic AI Systems
Architect and implement end‑to‑end agent workflows that decompose user intent into executable plans, intelligently select and invoke tools, and recover gracefully from errors and edge cases.
Rapidly prototype solutions and partner with engineering teams to drive production deployment
, including debugging live systems and building
AIOps workflows.
Use data and telemetry to identify AI quality gaps, generate insights, and deliver proofs of concept that apply research innovations to real‑world operational challenges.
Design robust multi‑step reasoning and tool‑use strategies
, including function calling, code execution, APIs, and secure connectors, with strong safety and reliability guardrails.
Drive production excellence across latency, reliability, cost efficiency, observability, monitoring, and safe fallback behaviors.
Own Evaluation, Metrics, and Iteration Loops
Define task‑level evaluation metrics for agentic behavior, including success rate, tool‑call accuracy, step efficiency, hallucination rate, safety violations, time‑to‑completion, and user satisfaction.
Build and maintain offline and online evaluation pipelines
, including: Golden datasets, scenario simulators, and regression test suites
Human‑in‑the‑loop evaluation frameworks and rubric design
A/B experimentation and telemetry‑driven iteration loops
Collaborate Across Disciplines and Lead Technical Execution
Partner with business and engineering stakeholders to translate requirements into clear technical specifications, research plans, and delivery milestones.
Lead design reviews and influence engineering decisions across agent frameworks, model integration, and system architecture.
Mentor and support other applied scientists and engineers, raising the bar for applied AI execution and production quality

PhD in Computer Science, Machine Learning, AI, NLP, or related field, OR
MS with significant experience delivering production‑grade AI systems.
10+ years of hands‑on experience applying NLP, and/or LLMs to real‑world production problems.
Strong foundation in Large Language Models (LLMs)
, Generative AI, deep learning, and modern NLP.
Proven experience building production‑grade LLM systems
, including prompt engineering, RAG, and multi‑step reasoning pipelines.
Solid expertise in chunking strategies and embedding selection
, including tradeoffs in retrieval quality, context size, and downstream task performance.
Experience designing and operating vector databases
, including indexing strategies, similarity metrics, and retrieval optimization.
Experience supporting production operations (AIOps/MLOps)
, including monitoring, logging, quality metrics, and debugging live systems.
Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and model serving/inference.
Strong applied experimentation skills: defining metrics, analyzing results, and iterating based on data.
Ability to collaborate effectively with engineering, product, and business stakeholders.
Experience with Cloud technology Stack
Experience with parameter‑efficient fine‑tuning techniques (e.g., LoRA) for LLMs or multimodal models.
Hands‑on experience building agentic or tool‑augmented LLM systems
, including function calling, planners, and API/tool integration.
Experience with advanced RAG architectures
, such as hybrid retrieval, reranking, grounding, and citation strategies.
Strong
AIOps depth
, including quality drift detection, alerting, rollback, and telemetry‑driven optimization.
Experience optimizing systems for latency, reliability, scalability, and cost efficiency at enterprise scale.
Prior experience working on mission‑critical or large‑scale enterprise AI systems.
Demonstrated mentorship or technical leadership within applied science or engineering teams.
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Principal Applied Scientist

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Equal opportunity and accommodations

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