ML Engineer

Role overview

An applied machine learning engineering role responsible for designing, building, and shipping the AI systems that power a software product platform. The work spans the full ML lifecycle — from selecting and fine-tuning models to running them reliably in production at scale.

Responsibilities

- Evaluate and select foundation or task-specific models suited to product use cases - Fine-tune, adapt, and optimize models to meet quality, latency, and cost targets - Build and maintain the training, serving, and monitoring infrastructure that runs models in production - Partner with product and platform engineers to integrate AI capabilities into the broader software stack - Establish evaluation, observability, and reliability practices for production AI systems

Requirements

- Professional experience as an ML engineer, applied AI engineer, or similar role - Hands-on background with model training, fine-tuning, and deployment workflows - Familiarity with production ML infrastructure, including serving, scaling, and monitoring - Strong programming skills in Python and modern ML frameworks - Ability to balance model quality with operational and cost constraints