Member of Technical Staff, Machine Learning & Optimization

Pepr AI•San Francisco, CA

65d•$160,000 - $250,000

About The Position

We are building the AI Operator for growth to replace the traditional ad agencies. We apply the quant rigor of high-frequency trading to optimize ad spend, delivering 20-50% upside in spend efficiency. We are managing spend for global category leaders like Cider and Cupshe with a clear path to managing billions of dollars. We are backed by Quiet Capital and are looking for early engineers. The Role We are looking for a Machine Learning Engineer to build the models, optimization systems and algorithms that drive our autonomous decision engine. You will not just build models; you will design the core financial strategies that dictate how millions of dollars are deployed and the execution layer that carries them out. Your role is to find inefficiencies in the ad markets, translate them into automated trading strategies and build the write-path integrations that execute those decisions in the real world. We are looking for builders who can bridge the gap between theoretical research and production systems. You are pragmatic enough to ship an 80% solution in a week to capture immediate value but disciplined enough to evolve that solution into a robust, generalized platform that manages risk at scale.

Requirements

A Senior Practitioner: You have 4+ years of experience applying machine learning and optimization to real-world problems. You understand that Deep Learning is a tool, not a religion, and you are equally comfortable with classical ML, regression analysis and control theory.
A System Builder: You view problems as opportunities to build platform capabilities, not one-off scripts. You have extensive experience deploying models into high-throughput production environments and know how to handle API rate limits, retries and failure states.
Math Fluent: You have a strong grasp of probability, statistics and linear algebra. You can read a research paper and implement it, but you know when a simple heuristic is the better engineering choice.
Pragmatic: You prioritize velocity and P&L impact. You ship fast to validate hypotheses and iterate based on live feedback from the market.

Nice To Haves

Mathematical Depth: You have taken a rigorous course in Convex Optimization and understand how to formulate and solve complex constraint problems.
AI-Native Workflow: You are an accelerator who uses tools like Cursor, Claude Code or Codex to write boilerplate and tests so you can focus on high-level logic.
Domain Experience: Experience in quantitative finance, algorithmic trading or programmatic advertising (RTB).

Responsibilities

Design the Core Algorithms: You will develop value models that predict customer LTV, risk-aware optimizers that allocate budget across channels and detection systems that identify creative fatigue before it kills performance.
Build the Execution Layer: You won't just predict the optimal bid; you will write the integrations that execute it. You will build the API connectors for Meta, Google and TikTok that robustly update bids, budgets and targets in real-time.
Deploy and Monitor at Scale: You own the model lifecycle from notebook to production. You will build the observability infrastructure to detect concept drift, monitor inference latency and ensure our trading decisions remain stable as market conditions change.
Backtest and Verify: Build the simulation infrastructure required to prove your strategies work before capital is deployed. You will ensure our logic is robust to market volatility and generalizes across different client verticals.