AI/ML Platform Engineer
Office - Washington-SeattleFull-TimeMid-levelSoftware Engineering
What You'll Do
- Platform Leadership: Lead the architecture, design, and implementation of the AI/ML feature platform, focusing on scalability, reliability, and high performance for core business systems (e.g., pricing, transaction platforms).
- Feature Engineering Infrastructure: Design and build large-scale data pipelines responsible for generating, indexing, and versioning AI-derived features (predictions, embeddings) from structured and unstructured data, with a specific emphasis on image and video data.
- AI Integration: Ensure seamless integration of ML capabilities into production, setting technical standards for real-time AI consumption and agentic optimization within pricing and customer-facing products.
- Cross-Functional Collaboration: Partner closely with Data Science, Product, and Operations teams to translate business needs into technical requirements, ensuring the platform delivers tangible business outcomes like reduced spreads and increased transaction volume.
- Mentorship & Direction: Serve as a technical mentor and leader, setting the technical direction and best practices for the new AI/ML platform group.
What You'll Need
- Extensive experience as a technical leader (Staff/Senior Staff level) in large-scale, production software and data environments.
- Proven track record of designing and implementing ML/Data Platforms or Feature Stores that directly support production AI/ML models.
- Deep expertise in distributed systems, cloud infrastructure, and large-scale data processing (e.g., vector search, data versioning).
- Strong background in data infrastructure and API design, particularly serving data for machine learning consumption.
- Experience in a domain where AI/ML is critical to the core business (e.g., pricing, optimization, recommendations).
Highly Sought-After Skills
- Prior experience with computer vision pipelines or handling large-scale image/video data for ML.
- Familiarity with the technical challenges of managing compute and data versioning to optimize ML iteration cycles.
- Experience with agentic optimization frameworks.
