Principal AI/ML Engineer

Summary

Join us as a Principal AI/ML Engineer and play a key role in designing, building, and scaling production-grade machine learning platforms. This leadership position requires driving innovation in AI/ML infrastructure, distributed systems, and data platforms. You will define the technical roadmap, lead implementation, and collaborate with cross-functional teams to operationalize real-time ML services at scale. The ideal candidate will have extensive experience in backend systems and machine learning infrastructure, proven ability to deploy ML systems in production, and strong leadership and communication skills. This is a full-time, permanent, remote position based in the Bay Area, Chicago, Boston, Portland ME, or Seattle.

Requirements

• 15+ years of software engineering experience with significant expertise in backend systems and machine learning infrastructure
• Hands-on experience with Spark, Ray, Kubernetes, Terraform, and AWS services such as EMR, S3, and Lambda
• Proficient in Java or Python, and comfortable contributing to codebase when needed
• Proven experience deploying ML systems in production—spanning supervised/unsupervised models, reinforcement learning, or large language models (LLMs)
• Track record of designing ML platforms and data pipelines in large-scale, cloud-native environments
• Demonstrated ability to mentor, lead, and collaborate cross-functionally at the enterprise level
• Strong communication and stakeholder influence skills

Responsibilities

• Architect and lead development of scalable AI/ML infrastructure and services across the enterprise
• Drive innovation in platform design, data engineering, observability, and ML system performance
• Lead end-to-end initiatives—from strategy to deployment—ensuring production-grade quality, security, and reliability
• Collaborate with Data Science, Security, Product, and SRE teams to deploy and monitor ML models (including LLMs and RL approaches)
• Champion modern engineering practices including CI/CD, Infrastructure as Code (Terraform, Kubernetes), and metrics-driven operations
• Mentor engineering talent and promote a high-performing, inclusive, and experimentation-driven culture
• Influence key architectural decisions and ensure the long-term scalability and maintainability of AI/ML systems

Preferred Qualifications

• Experience with SageMaker, Azure ML, or big data ecosystems (e.g., Hadoop, Hive, HBase)
• Background in payments, fintech, or risk-focused ML systems

Benefits

• Competitive base salary
• Bonus
• Equity
• Comprehensive benefits package