Senior Machine Learning Engineer

Primary: Bay Area (San Francisco / Peninsula)   |   Secondary: NYC

The Opportunity

We're doing an AI-first engineering rebuild for a company that already has an audience of 100M+ people. This is a zero-to-one build with no legacy constraints, so you get to stand up ML systems the right way from day one. You're here to ship machine learning that creates real, measurable value for a massive consumer audience.

The Product

You'll design, build, deploy, and operate ML systems that power the MrBeast ecosystem, bridging data science, software engineering, and platform engineering to ship production-grade capabilities. That means:

• Build scalable ML systems and services that move real business metrics for an audience of 100M+ people.
• Own the full lifecycle: pipelines for data processing, feature engineering, training, validation, deployment, and monitoring.
• Set the bar for AI-first engineering, including how we test new model capabilities and bring them into production.

What You'll Do

• Design and implement scalable ML systems and services for production.
• Develop, evaluate, and optimize models against real business problems.
• Build and maintain ML pipelines across data processing, features, training, validation, and deployment.
• Establish monitoring, observability, and model-performance tracking.
• Partner with product, data scientists, and software engineers to define and ship ML solutions.
• Drive architecture decisions for ML infrastructure and platform capabilities, and cut deployment cycle time.
• Mentor engineers, set best practices, and make sure systems meet security, reliability, and compliance bars.

Who You Are

• AI-Native: You live and breathe this: you're already burning through tokens daily, and shipping ML is the job itself.
• Production ML Builder: Typically 8+ years in software or ML engineering, with strong experience deploying and operating ML systems in production and solid Python and software engineering practice.
• Systems Thinker: You've designed scalable distributed systems and data-intensive applications, and you know why a model that looks great offline can fail in production.
• Evidence-Driven Owner: You decide with experimentation and measurable results, and you own outcomes from design through production operation.

Bonus points for MLOps platforms and automated model lifecycle management, cloud-native ML architectures and distributed training, responsible AI and model governance, and leading technical initiatives across multiple teams.

Benefits

• Equity: Highly competitive equity package designed for a foundational hire.
• Hybrid Model: Expected ~3 days per week in-office (Bay Area or NYC).