Software Engineer, ML Infrastructure, Level 4

About the role

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're looking for a Software Engineer to join the ML Platform Experience team, part of the core ML Platform organization. We are an AI native team which builds the agentic user experience for building, managing, and operating foundational models at Snapchat. We utilize Python as the primary language with Java/Go as supporting languages, we build agents on ADK, langfuse, and frontier LLM models, and are responsible for many other foundational technology such as model lineage, model orchestration, model data quality, and more.

Responsibilities

• Design and optimize infrastructure systems for machine learning workloads at scale and drive reliability and efficiency improvements across Snapchat's ML Infrastructure
• Build and enhance feature generation and serving pipelines that power online inferencing and offline training data generation
• Develop high-performance inference systems to ensure fast and efficient AI model serving
• Build infrastructure to perform scalable ML model training, evaluation, and inference in the cloud
• 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
• 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
• Experience with big data processing frameworks such as Spark, Flink, or Ray
• 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
• Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices

Minimum Qualifications

• Bachelor's degree in a technical field such as computer science or equivalent experience
• 2+ years of post-Bachelor's software development experience; or Master's degree in a technical field + 1+ year of post-grad software development experience; or PhD in a relevant technical field
• Experience building large scale production machine learning systems, distributed systems or big data processing

Preferred Qualifications

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

Pay

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

• Zone A (CA, WA, NYC): The base salary range for this position is $157,000-$235,000 annually.
• Zone B: The base salary range for this position is $149,000-$223,000 annually.
• Zone C: The base salary range for this position is $133,000-$200,000 annually.
• This position is eligible for equity in the form of RSUs.

Schedule

At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.