Staff Software Engineer (ML Infrastructure)

TL;DR

Staff Software Engineer (ML Infrastructure): Designing and managing scalable feature store systems and data pipelines for high-throughput and low-latency ML inference with an accent on system architecture, performance optimization, and operational reliability. Focus on building reusable abstractions, ensuring data consistency across serving paths, and mentoring engineering teams to raise the bar on technical craftsmanship.

Location: Must be based in the US and work from an office 4+ days per week (Bellevue, Palo Alto, or Seattle)

Company

Snap Inc. is a technology company focused on visual messaging and camera-based products like hirify.global, Bitmoji, and Specs.

What you will do

• Design and manage feature storage solutions for training-scale reads and low-latency serving lookups.
• Maintain a centralized, versioned feature registry to ensure definitions are discoverable and reusable.
• Build scalable, distributed systems with a focus on compute and storage efficiency.
• Ensure reliability through rigorous validation, testing, monitoring, and observability practices.
• Set technical direction, review designs, and mentor engineers to elevate team craftsmanship.
• Own the operational health of systems, including cost efficiency, on-call rotations, and incident response.

Requirements

• Must be based in the US and able to work from an office 4+ days per week.
• 9+ years of software development experience (or 8+ years with Master's, 5+ years with PhD).
• Deep experience designing, building, and operating backend services or distributed systems at scale.
• Strong foundation in system design, including APIs, service architecture, and workflow orchestration.
• Proven track record of owning highly available, mission-critical systems.
• Strong technical leadership skills with the ability to influence cross-functional initiatives.

Nice to have

• Deep expertise in modern C++ (C++11/14/17).
• Experience with Go, Java, or Python in large-scale production codebases.
• Experience building or scaling ML Infrastructure systems or real-time data pipelines.
• Proficiency with performance optimization techniques and platform architecture.

Culture & Benefits

• Comprehensive medical coverage and emotional/mental health support.
• Paid parental leave.
• Compensation packages including long-term success sharing.
• Collaborative environment with a "default together" office policy.