AI/ML Engineer

Role overview A full-stack AI engineering position focused on designing, building, training, and deploying the machine learning systems behind a real-time AI assistant. The work spans the entire ML lifecycle, from data preparation and model development through optimization, deployment, and ongoing production monitoring. The role bridges research and production engineering, pairing deep machine learning expertise with strong software engineering fundamentals.

Responsibilities - Design, train, and fine-tune language models and speech models for production use cases - Build and optimize inference pipelines to deliver real-time performance at scale - Develop retrieval-augmented generation (RAG) systems and the surrounding data infrastructure - Create evaluation frameworks that consistently measure AI quality and reliability - Ship production APIs and the monitoring tooling needed to track model behavior in production - Translate research ideas into shipped product features while balancing rigor and velocity

Requirements - Strong software engineering fundamentals alongside deep machine learning expertise - Hands-on experience designing transformer-based architectures and taking them to production - Familiarity with inference optimization, deployment patterns, and production observability - Ability to balance model quality, user impact, and shipping speed - Solid understanding of evaluation, testing, and monitoring practices for ML systems