Braze is looking to hire an engineer to help design, improve, and scale their self-learning (reinforcement learning) AI platform, implementing AI pipelines in production at scale.
Requirements
- Experience working with at least one major cloud platform (GCP, AWS, Azure, etc)
- Experience putting ML models into production
- 3+ years of experience working with Python in a product setting, including 1+ years in a the data/machine learning ecosystem
- If you have experience and/or like Ruby, this is a differential
- General understanding of supervised learning principles is a plus
- Stack: Python (Pyspark, Polars, Ibis), Ruby, SQL, BigQuery, FastAPI
- Architecture/DevOps: Kubernetes, Airflow, Terraform, GCP
Responsibilities
- Use robust software engineering best practices to design, implement, and improve modular components in a cutting-edge ML product
- Interact with big data, build for scale, and create the infrastructure to leverage all of its latent power.
- Apply extensive knowledge of Python and its ecosystem to produce clean, readable, and extendible code, and coach others on the team in doing the same
- Collaborate with teams responsible for Braze’s product strategy and roadmap
- Support teams implementing Braze for customers to ensure their success
- We write well-tested, type-hinted, documented, modular code and use pre-commit hooks, CI/CD, and issue tracking for development
Other
- Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
- If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back.
- Exceptional coder: you write clean, object-oriented code; you care about good design and terse, testable APIs
- Tinkerer: you regularly explore and learn new technologies and methods, especially in the data architecture and data science domains
- Entrepreneurial: you proactively identify opportunities and risks, work around obstacles, and always seek creative ways to improve processes and outcomes
