Join the Engine Room.

We are seeking exceptional, autonomous thinkers to build the future of advertising technology. Our team consists of former founders, engineers, and scientists from Y Combinator, Uber, Microsoft, and Snap.

Location: Remote (US/Canada preferred)
Type: Full-Time

Senior Full-Stack Engineer

Who We Are Looking For

We are seeking a versatile and experienced Senior Full-Stack Engineer who is comfortable working across the entire stack, from database design and API development to complex front-end interfaces. You will be instrumental in scaling our Agentic Media Engine and building robust, high-performance, and secure applications that handle millions of data points daily.

What You Will Do (Responsibilities)

  • Design, develop, and deploy highly scalable backend services (using Node.js/TypeScript) and relational databases (PostgreSQL).
  • Build high-fidelity, responsive, and complex front-end interfaces and data visualizations (using React/Next.js).
  • Lead the technical implementation of new features, from initial architecture design to deployment.
  • Collaborate closely with data scientists to productionize machine learning models into real-time decision-making APIs.
  • Implement and maintain robust unit and integration tests to ensure code quality and reliability.

What You Bring (Requirements)

  • 5+ years of professional software development experience.
  • Deep expertise in **JavaScript/TypeScript**, **Node.js**, and modern front-end frameworks like **React or Next.js**.
  • Strong command of **SQL** (preferably PostgreSQL) and experience with database design, optimization, and ORMs.
  • Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) is a strong plus.
  • A passion for building high-quality, maintainable software and a proven track record of solving complex technical problems.

Compensation and Benefits

We offer a competitive salary, significant equity package, and a comprehensive benefits package tailored for a remote-first company.

Ready to Apply?

Please send your résumé and a brief note about a technical problem you've solved recently to careers@9fs.ai.

Interview Process:

  1. Initial conversation with our founding team (30 min)
  2. Technical screen - coding and system design (60 min)
  3. Deep dive technical interview (90 min)
  4. Team fit and values conversation (30 min)
  5. Reference checks and offer

We move quickly and aim to complete the process in 1-2 weeks. We review applications daily and will respond within 48 hours.

Location: Remote
Type: Full-Time

Data Scientist - Marketing Recommendation Systems

About Us

We're a fast-growing startup building the future of marketing technology. We're at the stage where every team member has a massive impact, and we're looking for someone who wants to fundamentally innovate how people make decisions.

The Role

You'll architect our AI-powered recommendation engine that tells marketers exactly what to do with their budgets on a frequent cadence. This is a sophisticated decision system that combines Bayesian inference, causal measurement, and adaptive learning to generate actionable recommendations - specific guidance on where to spend, what creatives to run, and how to bid.

You'll collaborate closely with founding engineers, product managers, and leadership from successful tech companies who value rigorous technical work and data-driven decision making.

What You'll Work On

  • Build production recommendation systems that generate daily/weekly actionable guidance for marketing budget allocation with confidence scoring
  • Implement causal inference systems calibrated against geo-lift experiments, handling confounding, selection bias, and attribution gaming
  • Design Bayesian Time Varying Coefficient models to capture dynamic channel effectiveness and extract seasonality patterns for tactical recommendations
  • Architect cost-sensitive surrogate models that score creative and bidding strategy effectiveness (incremental vs. cannibalistic)
  • Build multi-armed bandit systems that balance exploration and exploitation while adapting to market changes and creative fatigue
  • Transform messy proprietary data streams (surveys, pixels, platform attribution, incrementality tests) into unified, high-conviction recommendations

What We're Looking For

Required:

  • 10+ years building production recommendation systems or decision algorithms
  • Deep expertise in Bayesian inference applied to real-world decision-making problems
  • Strong understanding of causal inference: confounding, selection bias, experimental design
  • Experience with probabilistic programming (PyMC, Stan, NumPyro) or Bayesian ML frameworks
  • Track record of shipping ML systems that generate actionable recommendations, not just predictions
  • Production-quality software engineering skills

Preferred:

  • Experience with multi-armed bandits, contextual bandits, or RL for decision systems
  • Background in recommendation systems, personalization engines, or algorithmic decision-making
  • Experience with incrementality testing, geo-experiments, or A/B testing frameworks
  • Familiarity with marketing measurement concepts (MMM, attribution, seasonality modeling)

What We Offer

  • Real Impact: Your algorithms will control millions in marketing spend and drive measurable ROI improvements
  • Technical Excellence: Work on genuinely hard problems at the intersection of causality, Bayesian inference, and real-time decision systems
  • Ownership: Lead the technical architecture of our core recommendation algorithm
  • Growth: Opportunity to grow into a senior or lead role as we scale from $1M to $100M ARR
  • Flexibility: Fully remote with async-friendly culture and flexible hours
  • Competitive Compensation: Salary commensurate with experience plus meaningful equity in a YC-backed company

Start Date

ASAP

How to Apply

Please email hi@9fs.ai with:

  • Your resume
  • Links to 2-3 projects that showcase your work (GitHub repos, technical blog posts, papers, or production systems—especially recommendation systems or causal inference applications)
  • A brief note (1 paragraph max) about a complex problem you've solved using Bayesian methods or causal inference to build a decision system, including the technical approach and business impact