TL;DR

Software Engineer, ML Infrastructure (AI): Designing and optimizing infrastructure systems for machine learning workloads at scale with an accent on driving reliability and efficiency improvements across %hirify_global%’s ML Infrastructure. Focus on developing high-performance inference systems and building infrastructure for scalable ML model training, evaluation, and inference.

Location: Onsite in Bellevue, Los Angeles, Palo Alto, San Francisco, or Seattle, United States. Team members are expected to work in an office 4+ days per week.

Salary: $157,000-$235,000 annually (base salary in Zone A: CA, WA, NYC).

Company

Snap Inc is a technology company focused on improving communication through camera-based products like %hirify_global%, Lens Studio, and Spectacles.

What you will do

Design and optimize ML infrastructure systems for scalability, reliability, and efficiency.
Build and enhance feature generation and serving pipelines for real-time and batch ML models.
Develop high-performance inference systems for fast and efficient AI model serving.
Build infrastructure for scalable ML model training, evaluation, and inference in the cloud.
Develop comprehensive data management systems for data collection, labeling, processing, and evaluation.
Collaborate with ML engineers to deploy cutting-edge models into production.

Requirements

Bachelor’s degree in computer science or equivalent experience.
2+ years of post-Bachelor’s software development experience, or Master’s degree + 1 year, or PhD in a relevant technical field.
Experience building large scale production machine learning systems, distributed systems, or big data processing.
Strong programming skills in Python, Java, Scala, or C++.
Good understanding of distributed systems and large-scale ML infrastructure components.
Proven track record of operating highly-available systems at significant scale.

Nice to have

Masters/PhD in a technical field.
Experience working with ML Training platforms or optimizing AI model inference.
Familiarity with ML frameworks such as TensorFlow, PyTorch, Spark ML, or scikit-learn.

Culture & Benefits

"Default Together" policy expecting 4+ days per week in the office.
Comprehensive medical coverage, emotional and mental health support programs.
Paid parental leave and compensation packages including equity (RSUs).
Commitment to diversity, inclusion, and equal opportunity employment.
Values of moving fast, with precision, and executing with privacy at the forefront.