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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** Braze - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $299,812.0 - **Contract:** Permanent contract - **Skills:** Artificial Neural Networks, Code Review, Distributed Systems, Python (Programming Language), Machine Learning, MongoDB, Ruby on Rails, Recommender Systems, Redis, Tensorflow, Azure Machine Learning, Pytorch, Kubernetes, Machine Learning Operations, Unsupervised Learning - **Published:** September 2, 2026 - **Apply:** https://job-boards.greenhouse.io/braze/jobs/8170742 ## About the Role * 8+ years building ML systems in production, with hands-on depth across data science, ML engineering, and ML operations. You have designed and trained models yourself, built the pipelines and services that run them, and operated them under production load * A technical leader who has owned direction for a team, led multi-quarter initiatives across team boundaries, and grown senior engineers, all while keeping a high personal output * Deep experience prototyping, refining, and deploying predictive models (supervised and unsupervised learning, neural networks, recommenders) with frameworks such as PyTorch and Tensorflow * Strong distributed systems fundamentals, designing for scale, reliability, and cost on the billions of daily data points our customers generate * An effective communicator, both verbal and written, whose designs and recommendations build consensus and drive forward decision making * Bonus: * Recommender systems, multi-armed bandits, or uplift modeling in production * ML platform tooling such as MLflow or another model registry, Ray, feature stores, or ML observability * Experience in our stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes) * Customer engagement, personalization, or marketing technology domain experience ## Description * Identify and drive the transformative initiatives that change what the team can deliver, whether that's replatforming how we train and serve models, redefining how data science ships to production, or retiring a generation of infrastructure * Build and ship at high velocity. Staff at Braze is a hands-on delivery role; you carry the most complex initiatives yourself from design through production. Current examples include distributed model training and serving, model lifecycle management, and the pipelines that keep hundreds of customer-specific models healthy across regions * Own the team's technical vision and quality bar. Set direction across the product portfolio and the ML platform, define best practices, and anticipate problems before they reach production * Drive initiatives that span teams. Our solutions ship into messaging, analytics, and data platform surfaces, and you carry the technical relationships with those teams * 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 * Connect technical decisions to customer and business outcomes, and represent the team's technical perspective to product and engineering leadership ## Related Videos - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [40 Minutes to Build a Serverless COVID-19 REST and GraphQL APIs](https://www.wearedevelopers.com/videos/208-40-minutes-to-build-a-serverless-covid-19-rest-and-graphql-apis) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)