Member of Technical Staff, Senior Applied AI Engineer

Microsoft•Redmond, WA

About The Position

We’re hiring a Senior Applied AI Engineer to join a fast‑moving, high‑ownership team building next‑generation AI assistant and productivity capabilities. This role blends LLM product engineering, evaluation science, hillclimbing, and internal tool building with the pace and creativity of a startup. You’ll work across the entire lifecycle of features from early prototypes to production‑grade systems and help define how millions of users interact with AI.

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.

Nice To Haves

Master’s Degree AND 3+ years of experience in engineering, problem solving, model building, evaluation, data analysis OR equivalent experience.
2+ years shipping production-level code, models, or data analysis.
1+ years using AI-assisted coding and analysis techniques.
Experience working on small teams and mid-stage startup environments.
Experience working on AI products.
PhD in engineering, applied math, statistics, or related analytical field.
4+ years shipping production-level code, models, or data analysis.
Deep experience building from zero-to-one.
Hands on work hillclimbing AI evaluations.

Responsibilities

Design and ship LLM‑powered assistant features, including conversational flows, agentic behaviors, retrieval pipelines, and multimodal interactions.
Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency.
Prototype new capabilities rapidly and iterate based on user signals and evaluation data.
Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality.
Run hillclimbing loops across prompts, models, and tool‑use strategies to continuously improve assistant performance.
Analyze failure modes, design mitigations, and drive systematic improvements across the stack.
Develop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelines
Create reusable frameworks that accelerate the entire AI org’s ability to ship high‑quality assistant features.
Integrate LLMs with product surfaces, APIs, and backend systems.
Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence.
Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.
Operate with startup‑founder energy: bias for action, rapid iteration, and comfort with ambiguity.
Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate.
Contribute to a culture of experimentation, clarity, and high‑quality execution.
Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency.
Prototype new capabilities rapidly and iterate based on user signals and evaluation data.
Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality.
Run hillclimbing loops across prompts, models, and tool‑use strategies to continuously improve assistant performance.
Analyze failure modes, design mitigations, and drive systematic improvements across the stack.
Develop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelines
Create reusable frameworks that accelerate the entire AI org’s ability to ship high‑quality assistant features.
Integrate LLMs with product surfaces, APIs, and backend systems.
Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence.
Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.
Operate with startup‑founder energy: bias for action, rapid iteration, and comfort with ambiguity.
Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate.
Contribute to a culture of experimentation, clarity, and high‑quality execution.