Staff Machine Learning Engineer (Commerce & Growth Intelligence)

Quick Facts

• Role: Staff Machine Learning Engineer (Commerce & Growth Intelligence)
• Focus: Model inference and shared ML infrastructure for customer-facing products
• Style: Hands-on technical leadership with architectural ownership and cross-team delivery

Description

Lead engineering projects across the AI/ML stack to turn emerging modeling capabilities into production software. You will own the engineering systems and integrations for training, deployment, and product usage—selecting/extending open-source infrastructure and designing component interfaces while balancing performance, reliability, compute, and deployment constraints. You can extend work into training, fine-tuning, data processing, and device-to-cloud integration.

Responsibilities

• Lead engineering projects across modeling, infrastructure, and product teams
• Build and operate reliable model inference services and shared ML infrastructure
• Enable fine-tuning workflows to handle larger datasets and workloads
• Choose and extend open-source infrastructure and design interfaces between components
• Resolve practical constraints across performance, reliability, compute resources, and deployment environments
• Drive technical initiatives across teams and deliver working systems

Requirements

• Experience shipping production ML systems or ML-powered applications with ownership of significant architecture and implementation decisions
• Deep expertise in at least one relevant area: model inference, distributed computing, ML infrastructure, or ML data systems
• Strong programming and software engineering skills: system design, API design, testing, debugging
• Practical experience integrating ML models into software systems and understanding execution and deployment requirements
• Track record leading technical initiatives across teams and resolving architectural tradeoffs
• Experience investigating performance and reliability problems and improving based on measured results
• Ability to communicate technical decisions clearly and build alignment across disciplines
• Bachelor’s degree in CS/CE/IS/Electronic Engineering with 12+ years of relevant work experience

Benefits

• Hands-on role with ownership of architectural decisions and delivery across teams
• Opportunity to expand from inference and shared infrastructure into training, fine-tuning, data processing, and device-to-cloud integration
• Work across model inference, distributed ML workloads, data processing, and cloud plus on-device intelligence on Apple devices