Data Science Engineer Jobs in San Francisco, CA
Data Science Engineer jobs in San Francisco are concentrated in SoMa, Mission Bay, and the Financial District, with strong demand across enterprise technology, fintech, and biotech. Employers actively hiring include Adobe, Lyft, and GoFundMe. See the openings below and apply to the ones that match your experience.
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About us
Beast Industries is a multifaceted media and entertainment company founded by Jimmy Donaldson, popularly known as MrBeast, the most watched person in the world. Renowned for revolutionizing digital content creation, Beast Industries encompasses a diverse portfolio of ventures that extend far beyond its origins on YouTube. With a mission to entertain, inspire, and create significant social impact, Beast Industries operates across various domains including digital media, philanthropy, consumer products, and innovative business initiatives. At Beast Industries, we believe in the transformative power of digital media and its potential to entertain, educate, and effect positive change. Our commitment to innovation, creativity, and philanthropy drives us to explore new frontiers, create unforgettable experiences, and build a legacy that inspires future generations.
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.
- 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.
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Find Data Science Engineer JobsData Science Engineer Job Market in San Francisco
Who's Hiring
- Adobe5

- Lyft3

- GoFundMe2

- Genentech2

- Stitch Fix2

Top Industries Hiring
- Technology & Software21
- Retail3
- Fintech2
- Medical Devices2
- Distribution & Wholesale2
Data Science Engineer Jobs in San Francisco: Frequently Asked Questions
How do I get a data science engineer job in San Francisco?
Focus your search on San Francisco's technology and fintech corridors, where the heaviest hiring concentrates in SoMa, Mission Bay, and around Salesforce Tower. Startups in the Dogpatch and growth-stage companies in the Financial District also hire consistently. Candidates who combine strong ML engineering skills with experience deploying models at scale stand out in this market, and familiarity with the cloud-native stacks common at Bay Area product companies gives a meaningful edge.
Which companies hire data science engineers in San Francisco?
San Francisco data science engineer roles are posted by Adobe, Lyft, and GoFundMe and others right now, based on current listings on Migrate Mate as of June 2026. San Francisco's employer mix skews toward product-led technology firms, AI-native startups, and financial services companies that maintain engineering hubs in the city itself.
Are there remote data science engineer jobs in San Francisco?
Yes, data science engineering is well-suited to remote and hybrid arrangements given that the work is largely analytical and code-based rather than on-site or equipment-dependent. About 67% of data science engineer openings tied to San Francisco are remote or hybrid as of June 2026, reflecting how broadly Bay Area employers have adopted flexible models. Model training, pipeline development, and research roles are most frequently offered with remote flexibility.
How can I get a data science engineer job in San Francisco with little or no experience?
The most realistic entry path in San Francisco is through junior or associate data engineering roles at mid-size technology companies and fintech firms that actively invest in developing early-career talent. Many San Francisco companies also hire from university research partnerships and fellowship programs at local institutions. Building a public portfolio of end-to-end ML pipelines and contributing to open-source projects referenced by Bay Area employers can compensate for limited professional experience and gets attention from engineering hiring teams.
Which industries hire the most data science engineers in San Francisco?
The sectors hiring the most data science engineers in San Francisco are Technology & Software, Retail, and Fintech, based on current listings on Migrate Mate as of June 2026. San Francisco's role as a hub for enterprise software, AI development, and financial technology means those industries maintain high and consistent demand for engineering talent with a strong data and machine learning foundation.
Related Jobs in California
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