Member of Technical Staff — Machine Learning & Optimization

📍 San Francisco | 💰 $180K–$250K + Equity

An early-stage AI company is building an autonomous decision engine to optimize large-scale digital marketing spend using machine learning and quantitative optimization techniques.

We’re looking for a Machine Learning Engineer / MTS to design predictive models, optimization algorithms, and production systems that drive automated decision-making across digital platforms.

You’ll work at the intersection of machine learning, optimization, and real-world execution systems, developing models that influence how significant budgets are allocated and deployed.

What You’ll Do

Develop predictive models that estimate value and inform decision strategies
Build optimization systems that allocate resources across competing objectives
Design infrastructure that executes algorithmic decisions through production systems and APIs
Create simulation and testing environments to evaluate new strategies before deployment
Own the lifecycle of ML systems from experimentation through production monitoring

Who You Are

4+ years of experience applying machine learning to real-world problems
Strong understanding of statistics, probability, and mathematical modeling
Experience deploying models into production environments
Comfortable building scalable systems and integrating with external APIs
Able to rapidly prototype solutions and iterate based on real-world performance

Nice to Have

Experience with optimization techniques such as convex optimization
Background in quantitative systems, marketplaces, or algorithmic decision systems
Experience working in early-stage startup environments

Compensation & Benefits

$180,000 – $250,000 salary
Meaningful early-stage equity
Health, dental, and vision coverage
Flexible PTO
Relocation support