Staff Software Engineer, ML Feature Store

Snap•Bellevue, NE

8d•Hybrid

About The Position

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company operates Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc. , a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji , Saturn, and other digital services. We’re looking for a Staff Software Engineer to join Snap Inc on our Feature Store team! This tech lead will be responsible for the design of the team's systems and data pipelines, establishing clear abstractions and reusable components so the team builds on common foundations rather than duplicating work. You'll drive development of the core services and data flows — including how data is stored, retrieved and served for both high-throughput and low-latency use cases — while ensuring they meet correctness, performance, and reliability requirements. A central part of the role is enforcing engineering quality through validation, testing, monitoring, and observability and owning the operational health of these systems from cost efficiency to on-call and incident response. Throughout, you'll set technical direction, review designs and code, and mentor engineers, raising the bar on craftsmanship across the team.

Requirements

Deep experience designing, building and operating backend services or distributed systems at significant scale.
Strong technical leadership skills, with the ability to set vision, ideate high-impact projects and translate strategy into executable roadmaps.
Proven ability to lead complex, cross-functional initiatives over multiple quarters while balancing architectural quality, reliability and delivery velocity.
Strong foundation in system design, including APIs, service architecture, compute platforms, storage concepts, observability and workflow orchestration.
Proven track record of owning highly available, mission-critical systems, including operational readiness, incident response and systemic improvements.
Strong judgment in making technical trade-offs and prioritizing the right long-term platform investments for a large engineering organization.
Excellent collaboration and communication skills, with the ability to influence engineers and leaders across Senior Manager- or Director-level organizations.
Ability to mentor, unblock and elevate other engineers while creating structures and mechanisms that make teams more effective over time.
Comfort operating in ambiguity and driving clarity in technically complex problem spaces.
Familiarity with responsible use of emerging AI technologies to improve engineering quality, alignment and innovation at scale.
Bachelor’s degree in a technical field such as Computer Science or equivalent practical experience
9+ years of software development experience; or Master’s degree with 8+ years of experience; or PhD with 5+ years of experience
Experience as a technical lead, domain expert or owner of complex technical initiatives
Experience building large scale storage, backend or distributed systems

Nice To Haves

Deep expertise in modern C++ (C++11/14/17), with experience in large-scale production codebases
Experience with C++, Go, Java, Python or similar backend languages.
Experience with large-scale microservices, cloud infrastructure and/or platform architecture
Proficiency with performance optimization techniques
Experience building or scaling ML Infrastructure systems and/or real-time data pipelines
Strong CS fundamentals (algorithms and data structures) and problem-solving skills
Comfortable working in a fast-paced, iterative and highly collaborative environment
Knowledge of software design patterns and best practices

Responsibilities

Serve features reliably for large-scale batch inference and low-latency online inference, maintaining consistency across serving paths and meeting latency and freshness requirements of production models.
Maintain features in a centralized, versioned feature registry, ensuring definitions are discoverable, well-documented and reusable across teams and models so common logic is defined once rather than duplicated.
Design and manage feature storage solutions for correctness, freshness and efficient retrieval, selecting appropriate storage patterns for training-scale reads and low-latency serving lookups.
Build scalable, distributed systems with attention to compute and storage efficiency, backed by monitoring, alerting, and sound operational practices that keep feature serving systems reliable and maintainable in production.