Member of Technical Staff, Product

Department: Engineering

Location: San Francisco

Compensation: $140K – $190K • Offers Equity

Employment Type: FullTime

About Pepr AI

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 high-agency Product Engineer with a Machine Learning / Artificial Intelligence focus to own features end-to-end.

You are not here to write research papers; you are here to ship models. You will act as a force multiplier for our Senior MLEs. One week you might be tuning a regression model to predict campaign fatigue, and the next you might be engineering a Generative AI pipeline to parse unstructured creative data.

We are looking for builders who are comfortable blurring the line between "Model Training" and "Software Engineering." You will own the model, the data pipeline that feeds it, and the API that serves it.

What You'll Do

• Ship ML Features End-to-End: You will take abstract product requirements and solve them with data. You will build, train, and deploy models, handling everything from data selection to the inference API.

• Build Generative AI Workflows: You will integrate LLMs to make sense of the chaos. You will build pipelines that use Generative AI to structure unstructured data (images, ad copy, competitor insights) into quantitative signals our core math models can use.

• Operationalize Intelligence: You will work closely with our Senior MLEs to turn mathematical insights into robust software. You will help maintain the training pipelines, feature stores, and evaluation loops that keep our system smart 24/7.

• Iterate on Model Performance: You will monitor live model performance and ship rapid improvements. You will run experiments to reduce latency, improve accuracy, or adapt our logic to new data inputs.

Who You Are

• An Applied ML Engineer: You have 2-5 years of experience building and deploying ML models. You are comfortable working with Python and standard libraries (Pandas, Scikit-learn, PyTorch).

• ML & GenAI Hybrid: You have experience with either classical Machine Learning (Regression, Classification, Time-Series) or building applications with LLMs (OpenAI, Anthropic, Vector DBs). You do not need to be an expert in both, but you must be eager to work across the spectrum.

• Product-First Mindset: You care about the outcome, not just the architecture. You view ML as a tool to solve user problems. You are happy to use a simple heuristic if it solves the problem better than a complex neural net.

• High Agency: You don't wait for permission or perfect datasets. When you see a data quality issue or a missing signal, you write the code to fix it yourself.

Bonus Points

• AI-Native Workflow: You use tools like Cursor, Claude Code or Codex to accelerate your development speed.

• Math/Stats Interest: You are curious about how probability and statistics influence system design and decision-making.

Compensation & Benefits

• Salary: $140,000 – $190,000

• Equity: Significant equity package

• Food: Daily lunch and (optional) dinner

• Relocation: Relocation support for candidates moving to the Bay Area

• Benefits: Comprehensive health, dental, vision and unlimited PTO