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's three core products are Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles .
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.
Snap ML Feature Generation Team is responsible for building the declarative ML Feature Generation platform at Snap . The platform allows users to declare new feature specs, register them into Feature Registry, read data from different data sources by processing 10+ trillion events daily, generate final feature values and vend them for online model inference and offline model training. We are solving two current major pain points from our MLE community: 1) accelerate feature onboarding velocity 2) improve feature generation freshness. For future focused areas, the team is designing new capabilities like Embedding Generation that unlocks universal user understanding strategies across Snap.
We're looking for a Staff Software Engineer to join the ML Feature Generation Team at Snap Inc!
We are looking for an Engineer Lead who has deep knowledge about ML data processing pipelines and is excited to contribute in the Machine Learning Infra domain. The engineer will drive technical direction for the team to solve the top challenges described above. We also expect the staff engineer to coach the rest of team members technically to work together on building new ML feature platform capabilities and improve system efficiency.
What you'll do:
- Drive technical direction for the team to accelerate ML iteration speed
- Identify opportunities by leveraging deep knowledge in big data & ML Infra systems to improve system performance and efficiency
- Coach the rest of team members and grow them technically
Knowledge, Skills & Abilities:
- Deep knowledge about data processing pipelines
- Proven track record of operating highly-available systems at significant scale
- Experience in at least one of the following areas:
- Big data systems (Spark/Flink)
- ML Infra systems (Inference/Training/Feature)
Minimum Qualifications:
- BS/BA degree in a technical field such as Computer Science or equivalent years of experience
- 11+ years of software development experience
- Experience with Java, Golang
- Experience working with distributed ML applications in production
Preferred Qualifications:
- Deep knowledge in ML development lifecycles
- Expertise in Spark or Flink, BigQuery, Iceberg and Kafka
If you have a disability or special need that requires accommodation, please don't be shy and provide us some information .
"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.
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.
Zone A (CA, WA, NYC) :
The base salary range for this position is $222,000-$333,000 annually.
Zone B :
The base salary range for this position is $211,000-$316,000 annually.
Zone C :
The base salary range for this position is $189,000-$283,000 annually.
This position is eligible for equity in the form of RSUs.
What We Do
Snap Inc. is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. We contribute to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.
Why Work With Us
Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.
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Snap Inc. Teams
Snap Inc. Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Our “default together” approach is an 80/20 model where we are asking team members to spend 80% of the time, on average, in the office, with the remaining 20% of the time spent remote.