Software Engineer, ML Infrastructure (AI)

TL;DR

Software Engineer, ML Infrastructure (AI): Design and optimize infrastructure systems for machine learning workloads at scale with an accent on feature generation pipelines and high-performance inference systems. Focus on building scalable ML model training, evaluation, and inference in the cloud.

Location: Hybrid (Bellevue, Palo Alto); must work in office 4+ days per week

Company

Snap Inc. is a technology company focused on visual communication through products like hirify.global, Lens Studio, and Spectacles.

What you will do

• Design and optimize large-scale infrastructure for ML workloads to improve reliability and efficiency.
• Build feature generation and serving pipelines for online inferencing and offline training.
• Develop high-performance inference systems for efficient AI model serving.
• Construct cloud-based infrastructure for scalable ML model training and evaluation.
• Create data management systems for scalable data collection, labeling, and processing.
• 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 1+ with Master's/PhD).
• Experience building large-scale production ML systems, distributed systems, or big data processing.
• Strong programming skills in Python, Java, Scala, or C++.
• Deep understanding of distributed systems and large-scale ML infrastructure components.

Nice to have

• Masters or PhD in a technical field such as computer science.
• Experience working with ML Training platforms or optimizing AI model inference.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Caffe2, Spark ML, or scikit-learn.

Culture & Benefits

• Paid parental leave and comprehensive medical coverage.
• Emotional and mental health support programs.
• Compensation packages that allow sharing in the company's long-term success.
• "Default Together" culture emphasizing dynamic in-person collaboration.