Job Description

Data Science 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 the models and data systems you ship define the foundation instead of patching an old one. You're here to turn ambiguous, high-stakes business problems into models that actually move a number in production.

The Product

You'll be the senior technical anchor for a data science domain, owning the full lifecycle from framing the problem through deployment, monitoring, and iteration. The work spans consumer products, media, and fintech analytics, all sitting on top of an audience of 100M+ people. That means:

• Frame vague business problems as tractable data science problems, and pick the approach and evaluation criteria when there's little precedent.
• Design, build, and deploy models and the data pipelines that feed and serve them in production.
• Build the monitoring and retraining framework that catches drift before it hits the business.

What You'll Do

• Own the full model lifecycle: data sourcing and quality, features, training, evaluation, deployment, monitoring, and retraining.
• Set and enforce the domain's standards for validation, reproducibility, experimentation, and monitoring.
• Partner with engineering to productionize models reliably, with the right latency, scale, and observability.
• Translate model behavior and its limits for product and business stakeholders, including where data science can't help.
• Anticipate the failure modes (leakage, drift, bias, fragility) and build safeguards before they reach production.
• Guide the technical work of other data scientists and engineers through design review, pairing, and mentorship.
• Evaluate and adopt new methods and tooling, weighing innovation against maintainability and cost.

Who You Are

• AI-Native: You're already burning through tokens and using AI in your daily workflow to move faster from idea to shipped model.
• Production ML Builder: Typically 8+ years designing, building, and deploying ML models in production, with deep expertise in statistical modeling and sound judgment about method selection under uncertainty.
• End-to-End Owner: You've owned problems start to finish with limited supervision and been accountable for the result, not just the experiment.
• Honest Communicator: You frame problems as testable hypotheses, hold the line on validation rigor under deadline pressure, and communicate uncertainty honestly instead of overselling.

Strong software engineering practice: production-quality code, version control, testing, and reproducible pipelines. Bonus points for setting technical direction for a data science domain, MLOps tooling for deployment and monitoring, and domain exposure in consumer products, media, or fintech.

Benefits

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

The Perks — Why Work On the MrBeast Team

We are redefining what entertainment and storytelling look like at global scale. Every piece of content we publish reaches millions and influences culture in real time. This is your opportunity to join the team that decides how those moments come to life across every screen.

• Competitive Salary
• Generous Medical (Blue Cross Blue Shield), Dental, Vision and company-paid Life Insurance
• Company contributions to employee Health Savings Accounts (HSA)
• 401k Plan with Safe Harbor company-matching
• Flexible vacation policy and paid company holidays
• Company-provided technology package
• Relocation assistance where applicable, including travel and company-provided housing for the first 90 days

Come build the future of the creator ecosystem with us.