Students can post questions, memes, polls, and chat with other verified students while maintaining control over their identity and privacy. Fizz communities prioritize safety, authenticity, and inclusion through community-based moderation.
Founded by two Stanford dropouts, Fizz is currently expanding to colleges across the US with a mission to redefine the “social network” in a time when legacy platforms lack authenticity and are filled with curated highlight reels.
What we’ve done
Where we’re going
As a Fizz intern, you will be able to use your own experience of being a Fizz user to directly build for a product you care about. If you are proactive and driven to tackle challenging technical problems, have a passion for crafting high-quality software, and are eager to learn quickly and build new products from scratch, this internship is perfect for you. Interns will be compensated financially and work full time out of our Palo Alto office.
Responsibilities
- Prototype, design, develop and productionize Machine Learning solutions
- Design, develop and productionize data pipelines for solving real world ML problems
- Keep up to date with cutting-edge research in relevant fields and adapt solutions for large scale user facing problems
- Work with other engineers closely to integrate ML components to the product workflow
- Work closely with senior engineers to support and iterate fast on the solutions and measure the impact
Qualifications
- Currently enrolled in a Bachelors, Masters or Post Graduate degree in Computer Science, Statistics, Mathematics or relevant field
- Have strong Computer Science background with academic or real world experience in Machine Learning, Natural Language Processing, Computer Vision or other related fields
- Have theoretical understanding and academic experience in developing ML solutions approaches such as Tree models, Deep neural networks, Sequence modeling, Transformers, LLMs
- Familiarity with PyTorch, TensorFlow or other relevant DL frameworks
Bonus Points
- Experience working with User-Generated Content
- Familiarity with large-scale systems and data such as Hadoop, Hive, Spark, Kafka
- Familiarity with GCP, Vertex AI and other GCP offerings