Senior Algorithms Engineer

What is a Senior Algorithms Engineer?

At Keen, the Senior Algorithms Engineer develops, deploys and improves the algorithms that run in Keen AI Cortex. You're a software engineer first, with the understanding of mathematics, data, and inference to read a model spec, question it, and suggest changes the Principal Algorithms Architect hadn't considered. You'll work directly with the Lead Engineer, and Principal Algorithms Architect, taking modeling ideas from prototype to production code and contributing to the algorithms when you see a better path.

Requirements

What you’ll do as the Senior Algorithms Engineer

• Own algorithmic features end-to-end across the next generation of Keen's AI Cortex. Design, implement, test, ship, and watch them run in production.
• Pitch in on adjacent Python backend projects (Django and FastAPI APIs, AWS infrastructure) when a piece of work spans more than one service.
• Keep the engineering practices tight: code review, tests, CI gates, documentation, and a clean ticketed workflow.
• Use AI coding tools with judgment. Fast iteration is the point. So is catching the moments when the model is confidently wrong.
• Develop algorithms from business ideas and turn them into production code, Collaborate with others in the development of the solutions, including the ability to effectively push back when the implementation can be improved.
• Communicate clearly across Product, Research, and Engineering. Write the design doc, name the tradeoff, get the decision made.
• What Tech Stack do we choose?
• Google Suite for mail / calendar
• Jira for project management
• PostgreSQL, SQLite, pandas, NumPy
• AWS, Docker, GitHub Actions, pytest
• Solvers such as Ipopt, GAMS
• Python 3+ Django, FastAPI, REST APIs
• Slack for internal communications
• What expertise do the best Senior Algorithms Engineers for Keen have?
• Must-have: 7+ years shipping production Python on a team
• Knowledge of inference methods, particularly in economics and business domains including time-series models (or more generally state-space modeling), probability modeling, and multivariate systems.
• Comfortable with the engineering basics: tests, CI, code review, documentation, and working out of tickets
• Hands-on experience with AWS, Docker
• Operates independently. Scopes own work, talks to stakeholders, makes judgment calls when no one else is available to make them
• Writes and speaks clearly
• Math foundation in linear algebra, probability, and statistics. Demonstrated ability to read and write mathematical specifications, interpret and improve documented solutions.
• Working knowledge of pandas and NumPy, with understanding of the use of these packages in implementing algorithms.
• Nice-to-have: Marketing Mix Modeling (MMM) or another econometric background
• Bayesian inference experience
• Optimization exposure
• A track record of developing mathematical algorithms into shipped production systems.