Machine Learning Engineer

Member of Technical Staff, Machine Learning & Optimization

San Francisco Bay Area (On-site)

📍 Onsite – San Francisco

💰 $160k–250k + Equity

Exciting opportunity with Series A startup, building out autonomous AI systems for performance marketing.

Their platform applies ideas from quantitative trading, optimization, and risk management to digital advertising markets, automatically managing ad spend across large-scale campaigns.

The team is already working with major global consumer brands and consistently improving advertising efficiency at scale.

They’re hiring a Member of Technical Staff focused on machine learning and optimization systems.

This is a highly hands-on role with end-to-end ownership across modeling, infrastructure, and execution systems.

Responsibilities ⚙️

• Build ML and optimization systems for bidding, budget allocation, and campaign performance

• Design algorithms that identify inefficiencies and improve capital allocation across ad platforms

• Build integrations with platforms including Meta, Google, and TikTok

• Deploy and monitor production ML systems in real-time environments

• Develop simulation and backtesting infrastructure for strategy validation

• Work across data pipelines, inference systems, and execution layers

Requirements

• 4+ years of machine learning engineering experience

• Strong Python and production ML systems experience

• Experience with optimization, forecasting, ranking, or recommendation systems

• Strong understanding of probability, statistics, and applied mathematics

• Experience deploying high-throughput production systems

• Comfortable working in fast-moving, high-ownership startup environments

Direct Bonus Requirements

• Background in quantitative finance, trading systems, or ad-tech

• Experience with convex optimization or control systems

• Experience with reinforcement learning or decision systems

• Familiarity with AI-native developer tooling such as Cursor or Codex.

If interested, apply here or reach out directly to learn more.