# Staff Applied Scientist

**Company**: Braze
**Location**: Chicago
**Work arrangement**: hybrid
**Experience**: staff
**Job type**: full-time
**Salary**: $184,000 - $299,812/year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/braze/jobs/8170742?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_fce9eb25-fb1

## Description

Braze is seeking a Staff Applied Scientist to join our Predictive and Generative AI (PGAI) team.

The team's mission is to deliver a truly engaging and personalized customer experience through the creation of ML and AI enhanced marketing solutions.

As the Staff Engineer on the team, you will:

- Identify and drive transformative initiatives that change what the team can deliver

- Build and ship at high velocity, carrying complex initiatives from design through production

- Own the team's technical vision and quality bar, setting direction across the product portfolio and ML platform

- Drive initiatives that span teams, maintaining technical relationships with stakeholders

- Raise the team's engineering quality through design review, code review, and production readiness for ML systems, and mentor other senior engineers and data scientists

You are:

- A technical leader with 8+ years of experience building ML systems in production

- Hands-on depth across data science, ML engineering, and ML operations

- Experienced in prototyping, refining, and deploying predictive models with frameworks such as PyTorch and Tensorflow

- Strong distributed systems fundamentals, designing for scale, reliability, and cost

- An effective communicator, both verbal and written

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $184,000 and $299,812/year with an expected On Target Earnings (OTE) between $204,000 and $332,400/year.

## Skills

### Required
- ML systems
- data science
- ML engineering
- ML operations
- PyTorch
- Tensorflow
- distributed systems

### Nice to have
- Recommender systems
- multi-armed bandits
- uplift modeling
- MLflow
- Ray
- feature stores
- ML observability
- Python
- Ruby on Rails
- MongoDB
- Redis
- Kubernetes

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/braze/jobs/8170742?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)