Microsoft Principal AI Engineer 

Taiwan, Taoyuan City 

985345208

Required Qualifications:

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• 6+ years of experience driving complex, cross-functional initiatives; experience leading without authority across multiple teams.
• 3+ years working with Machine Learning (ML)/Artificial Intelligence (AI) systems (e.g., Large Language Models (LLMs)/Generative AI (GenAI), retrieval/Retrieval-Augmented Generation (RAG), model serving, experimentation platforms, data pipelines) including establishing evaluation metrics and improving model quality.

Other RequirementsAbility 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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preffered Qualifications:

• Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Experienced in program leadership, communication, and stakeholder management skills with the ability to influence leaders and make data-informed decisions.
• Proven track record shipping cloud services or platforms at scale (multi-tenant, high-throughput) with measurable customer and business impact.
• Security domain expertise (e.g., threat detection/response, SIEM/SOAR, identity, endpoint, cloud security) and familiarity with analyst workflows.
• Experience with GenAI/LLM techniques and tooling (prompt engineering, retrieval/vector stores, agents/tool use, content safety/guardrails, offline/online eval frameworks, vibe coding).
• Hands-on coding ability in one or more languages (e.g., Python, C#, C++, Rust, JavaScript/TypeScript); comfortable prototyping, reading Pull Requests (PRs), and engaging deeply in technical design reviews.
• Demonstrated success driving zero-to-one (0→1) initiatives from ambiguity to Minimum Viable Product (MVP) to General Availability (GA) and then to one-to-many (1→N) platform adoption across multiple product teams. What makes a great Principal AI Engineer here
• Model-literate and pragmatic: you know when to use a Large Language Model (LLM), when a deterministic/rules-based or classical Machine Learning (ML) approach is better, and how to hybridize them with retrieval, caching, routing, and fallbacks to meet Service Level Objectives (SLOs) and cost targets.
• Evaluation-obsessed: you can define the right metrics and datasets (clarity, groundedness, precision/recall, latency/cost etc.), build the eval harness, and insist on measurable improvements before broad rollout.
• Zero-to-one (0→1) builder and one-to-many (1→N) integrator: you turn ambiguous ideas into Minimum Viable Products (MVPs), then lead General Availability (GA) platformization and integration into large, multi-tenant Microsoft Security products. You define durable data/Application Programming Interface (API) contracts and Software Development Kits (SDKs), negotiate dependency graphs across partner teams to meet delivery dates and Service Level Objectives (SLOs) within complex cost, security/privacy/compliance constraints to ensure operational readiness.
• Customer-back and outcome-focused: you spend time with defenders and admins, translate workflows into crisp specs, land adoption, and iterate quickly based on feedback and telemetry.
• Clear communicator and connector: you create clarity in ambiguity, align diverse stakeholders across research/engineering/design/field, and mentor others to raise the bar.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Define the technical vision, strategy, and roadmap for AI-native incubation initiatives; align stakeholders across Security Copilot, Defender, Sentinel, Entra, Purview, Azure AI Foundry and Microsoft AI to deliver cohesive customer value.
• Lead zero-to-one (0→1) incubation R&D through MVP and private preview, then drive one-to-many (1→N) platformization and scale to GA; make principled tradeoffs across quality, latency, reliability, cost, and safety.
• Provide hands-on technical leadership: prototype in code, review designs and Pull Requests (PRs), define Application Programming Interfaces (APIs)/data contracts, build comprehensive well-architected systems, and establish evaluation frameworks to de-risk complex systems.
• Set strategy for AI-native security experiences and platform components: where to use Large Language Models (LLMs) versus classical Machine Learning (ML), retrieval/Retrieval-Augmented Generation (RAG) design, grounding, model routing/fallbacks, and safety guardrails to meet customer outcomes and Service Level Objectives (SLOs).
• Ensure Responsible AI, privacy, and security guardrails are designed in from day one, coordinate safety reviews, abuse prevention, compliance, and incident readiness.
• Lead v-teams and mentor others; cultivate a builder culture of velocity and quality as a force multiplier.
• Engage directly with enterprise customers and field to co-design solutions and land adoption; communicate program status and strategy to executives with hands on real code demonstrations.