Staff Software Engineer, ML Infrastructure, Level 6

Responsibilities

• Design and optimize infrastructure systems for machine learning workloads at scale and drive reliability and efficiency improvements across Snapchat's ML Infrastructure
• Develop high-performance embedding generation / batch inference systems to improve model performance
• Develop high-performance data storage/compute systems to improve the efficiency of our ML infrastructure
• Integrate state of the art ML data quality system to assure model performance
• Build comprehensive data management systems for scalable data collection, labeling, processing, and evaluation
• Work closely with ML engineers to deploy cutting-edge models into production
• 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 code
• Knowledge, Skills & Abilities:
• Strong programming skills in Python, Java, Scala, or C++
• Strong problem-solving skills with a focus on system performance, scalability, and efficiency
• Good understanding of distributed systems and the infrastructure components of large-scale ML
• Ability to collaborate and work well with others
• Proven track record of operating highly-available systems at significant scale
• Ability to proactively learn new concepts and apply them at work
• 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

Requirements

• Bachelor's degree in a technical field such as computer science or equivalent experience
• 9+ years of post-Bachelor's software development experience; or Master's degree in a technical field + 5+ years of post-grad software development experience; or PhD in a relevant technical field+ 2+ years of post-grad software development experience
• Experience building large scale production machine learning systems, distributed systems or big data processing
• Masters/PhD in a technical field such as computer science or equivalent industry experience
• Experience with big data processing frameworks such as Spark, Flink, or Ray
• Experience with large scale feature store or embedding system
• Familiarity with ML frameworks such as Pytorch, Tensorflow
• If you have a disability or special need that requires accommodation, please don't be shy and provide us some information .

Additional Information

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. You'll play a critical role in scaling our ML Infrastructure, optimizing our embedding, feature and training data storage and compute to enable different paradigms of ML models at massive scale, make Snapchat's ranking and recommendation systems more efficient and impactful. We're looking for a Staff Software Engineer, ML Infrastructure to join Snap Inc!

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at Snap? Share your experience