Overview
As a Machine Learning Engineer at Phia, you’ll help build and support the systems that enable reliable machine learning at scale, with a focus on buffering and data flow. You’ll work on foundational components that manage how data and model outputs move through training and production systems. This role is designed for early-career engineers who want hands-on experience with production ML infrastructure while learning from experienced ML and platform engineers. You’ll contribute to core systems that make experimentation, deployment, and iteration possible as Phia grows.
About Phia
Phia has raised $43M from Notable Capital, Khosla Ventures, and Kleiner Perkins to build the AI alignment layer for commerce. In under a year, Phia’s consumer shopping agent has surpassed one million users and partnered with 6,200+ retail brands, representing billions in annual gross merchandise volume. Each month, Phia drives millions of dollars in sales for brands and has achieved 11× revenue growth since launch.
In an era where AI vertical agents are reshaping every industry, commerce is on the verge of a complete transformation. Phia is reinventing shopping from a fragmented, impersonal experience into one that feels intelligent, trusted, and built around each user’s intent. This foundation of trust is our wedge to become the end-to-end shopping destination for the next generation of buyers.
Phia is a lean, high-ownership team building at startup speed. If you want to ship at high velocity and solve complex problems in consumer AI and commerce, this is the place to do it.
What You Own
- Support the development and maintenance of ML pipelines, data flows, and buffering systems
- Help implement and monitor models and services used across ML teams
- Partner with senior engineers to improve reliability and performance of ML systems
- Contribute to tooling that supports experimentation, training, and deployment
- Debug issues in data pipelines and model workflows
- Learn best practices for production ML, testing, and monitoring
Qualifications
- 1+ years of experience or equivalent coursework in machine learning, data engineering, or backend systems
- Proficiency in Python and familiarity with common ML or data processing libraries
- Basic understanding of data pipelines, model training workflows, and ML concepts
- Interest in infrastructure, scalability, and production systems
- Ability to learn quickly and work collaboratively with experienced engineers
- Clear communication and strong ownership mindset for an early-career role
Compensation Range: $175K - $215K