Requirements

• 12+ years of professional experience in machine learning engineering, applied ML, or a closely related discipline, with a track record of shipping ML systems to production at scale.
• Strong software engineering fundamentals, including experience building and operating distributed, production-grade systems.
• Proficiency with common ML frameworks and languages (e.g., Python, PyTorch/TensorFlow) and with MLOps practices (model versioning, CI/CD for ML, monitoring).
• Ability to work self-directed at a principal level — shaping technical direction, partnering on architecture, and guiding projects from concept to delivery.
• Proven track record of designing, evaluating, and deploying Generative AI models and Large Language Models (LLMs) in enterprise production environments.
• Strong collaboration and communication skills, with experience mentoring engineers and influencing cross-functional stakeholders.

Nice to Haves

• Experience working within large-scale, multi-tenant SaaS platforms.
• Background in applying ML to enterprise/administrative platform problems (e.g., anomaly detection, automation, intelligent configuration).
• Hands-on familiarity with LLM orchestration frameworks (preferably LangChain) and AI agentic architectures.
• Experience with cloud infrastructure and large-scale data processing systems.
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

What You'll Be Doing

• Design, build, and productionize machine learning models and pipelines that integrate with Workday's platform-level systems.
• Set technical direction and best practices for ML engineering within the team, including model development, evaluation, deployment, and monitoring.
• Partner with distributed-systems engineers to ensure ML solutions scale reliably across Workday's multi-tenant architecture.
• Lead and collaborate on end-to-end ML lifecycle work — from problem framing and data strategy through model training, deployment, and performance monitoring in production.
• Collaborate cross-functionally with product management, data engineering, and platform teams to identify high-impact opportunities for ML-driven automation and intelligence.
• Mentor and provide technical guidance to other engineers on ML system design, coding practices, and architectural decisions.
• Evaluate and introduce new tools, frameworks, and techniques to keep the team's ML practices current and effective.
• Contribute to the long-term technical roadmap and architecture for ML capabilities within the Ampere team and the broader organization.
• Lead the architectural design and deployment of Generative AI capabilities and Large Language Model (LLM) applications.

Perks and Benefits

• Flexible work approach combining in-person time and remote work with at least 50% of time spent each quarter in the office or in the field, enabling a flexible schedule.
• Opportunity to work at a Fortune 500 company with a culture rooted in integrity, empathy, and shared enthusiasm.
• Supportive environment with trust to take risks, tools and skills development, and long-term company investment in employees.
• Workday Bonus Plan or role-specific commission/bonus eligibility and annual refresh stock grants.
• Commitment to diversity, inclusion, and reasonable accommodations for qualified individuals during the application process and employment.