Manager, Software Engineering, ML Inference

Snap Inc.•Los Angeles, CA

1d•$195,000 - $343,000•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. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We're looking for a Manager, Software Engineering, ML Inference to join Snap Inc.!

Requirements

Strong understanding of ML infrastructure systems including model training platforms, inference serving, feature stores, and data pipelines
Background building high availability, mission-critical systems at significant scale
Experience setting technical direction for teams whose work directly enables ML engineers and production ML systems
Strong management and mentorship skills, with the ability to lead and grow senior engineers
Excellent verbal and written communication skills, with high attention to detail
Ability to collaborate with internal and external stakeholders at all levels
Skilled at managing ambiguous problems and driving clarity across complex, multi-team initiatives
Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks
Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices
Bachelor's degree in a technical field such as computer science or equivalent years of experience
9+ years of post-Bachelor's software engineering experience; or a Master's degree in a technical field + 8+ years of post-grad experience; or a PhD in a related technical field + 5+ years of post-grad experience
1+ year(s) of experience managing an engineering team
Experience with distributed systems and large-scale ML infrastructure

Nice To Haves

Advanced degree in a related technical field
Experience working with ML training platforms, inference infrastructure, or feature serving systems
Familiarity with ML frameworks such as TensorFlow, PyTorch, Caffe2, Spark ML, or related frameworks
Experience with Spark, Flink, Ray, or other big data processing technologies
Experience with key infrastructure technologies including Kubernetes, NoSQL, Memcache/Redis, Kafka, Google Cloud, or AWS services
Track record of delivery in rapidly changing, highly collaborative, multi-stakeholder environments
Experience with MLOps and managing production machine learning lifecycle

Responsibilities

Lead and mentor a team of ML Infrastructure engineers responsible for building and scaling the systems that power Snap's model training, inference, and data pipelines
Set the strategy, build a roadmap, create measurable goals, and lead your team to deliver high-impact ML infrastructure initiatives
Evaluate the technical tradeoffs of key decisions and serve as a strong technical mentor across the team
Perform design and code reviews to continuously raise the technical excellence bar
Collaborate with ML engineers, product teams, and cross-functional stakeholders to understand requirements, evaluate tradeoffs, and deliver solutions at scale
Hire, grow, and retain high-performing engineers by creating growth opportunities, giving regular feedback, and managing performance
Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management
Utilize AI tools and high velocity engineering workflows to design and ship scalable services while upholding rigorous standards for code correctness, security, and production-ready quality