Requirements

12+ years of experience designing, building, and operating large-scale machine learning systems in production environments
Experience architecting ML infrastructure components such as distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at a significant scale
Experience identifying and resolving systemic performance, reliability, or correctness issues that span multiple ML systems or abstraction layers
Experience influencing technical direction across multiple engineering teams through written proposals, design reviews, and cross-functional stakeholder alignment
Experience defining engineering standards, architectural patterns, or platform capabilities broadly adopted across an organization

Nice to Haves

Experience building large-scale object recognition models
Experience collaborating with applied research teams to translate theoretical ML advances into production-grade system improvements
Ongoing AI skill development such as prompt/context engineering and agent orchestration with knowledge of emerging AI technologies
Ability to integrate AI tools to optimize or redesign workflows for efficiency gains and quality improvements
Experience developing novel approaches to ML observability, experiment management, or model lifecycle tooling that improve engineering velocity
Experience leading multi-year ML platform or infrastructure migrations in complex, mature environments
Experience training large language models in production systems at scale
Experience implementing responsible, ethical AI practices including risk assessment, bias mitigation, and quality reviews

Tackle complex ML systems challenges including training efficiency, large-scale inference optimization, and pipeline reliability
Define and own technical architecture for critical ML infrastructure ensuring extensibility and maintainability
Establish engineering standards and invariants to prevent reliability, correctness, and performance issues
Drive cross-organizational technical strategy by assessing ML technologies and influencing roadmap decisions
Lead design and delivery of organization-wide ML platform initiatives coordinating with engineering, research, product, and data science teams
Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency
Mentor engineers on ML system design, debugging complex models and infrastructure failures, and creating scalable solutions
Collaborate with privacy, security, and compliance teams to design ML systems that uphold safety and meet regulatory requirements
Identify and remove engineering bottlenecks via tooling, automation, and process improvements
Contribute to recruiting by engaging candidates with ML systems expertise and evaluating senior engineering talent