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Software Engineer, ML Infrastructure, Core… at Snap · Palo Alto
Job Description
Software Engineer, ML Infrastructure, Core Inference, Level 4
Location: Palo Alto, California
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 Software Engineer to join the Core Inference team, part of the core ML Platform organization. Our team builds and owns the machine learning infrastructure that powers personalized experiences across Snap. As a centralized ML platform team, we enable teams across the company to deploy, optimize, and serve sophisticated machine learning models reliably and efficiently at scale.
We own the end-to-end inference stack for recommendation models, embedding-generation models, and self-hosted large language models. Our platform supports nearly every major organization at Snap, including Ads, Content, Growth, Lenses, Security, and My AI.
What you’ll do:
- Support the models powering personalized experiences across Discover, Stories, Spotlight, and other Snap products with ranking needs.
- Optimize, export, and prepare models for efficient production use while working across training and inference.
- Own the infrastructure used to host and serve recommendation models, embedding-generation models, and self-hosted LLMs.
- Work on LLM fine-tuning, reinforcement learning infrastructure, serving, and inference optimization.
- Provide model-hosting and serving capabilities to teams across Ads, Content, Growth, Lenses, Security, My AI, and other product areas.
- Own the inference components that power advertising models, while Ads teams generally own the ad products, formats, modeling objectives, and ranking strategies.
- Provide ranking and ML infrastructure for products such as Camera, Creative Tools, Maps, Chat, Memories, Games, and Minis when those products require model hosting or serving.
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
"Default Together" Policy at Snap: 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.
At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).
Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!
Compensation
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.
The base salary range for this position is $157,000-$235,000 annually.
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