Machine Learning Engineer

Key Responsibilities • Productizing ML Models: Bridge the gap between prototype and production by transforming theoretical ML models into robust, scalable, and highly performant user-facing software products. • Model & Application Development: Design, build, and optimize machine learning application layers, applying rigorous mathematical and statistical methodologies to ensure model accuracy and validity. • Back-End Engineering: Partner with the CTO and AI Director to develop robust Python-based back-end systems and server frameworks to support core AI features. • Architecture Definition: Take ownership of defining model architectures and integrating them seamlessly into our core product ecosystem. • Database & Security Management: Securely manage relational databases (PostgreSQL), ensuring strict application-level data security and compliance for sensitive health data. • MLOps & Infrastructure Support: Support the deployment and orchestration of models using Google Kubernetes Engine (GKE) on Google Cloud Platform (GCP), ensuring basic automated pipeline tracking and environment stability. • Cloud Security Compliance: Implement cloud security best practices, including access authorization and airgapping strategies to protect operational data environments. Required Skills & Qualifications • Experience: Proven track record with 5+ years of software development experience heavily focused on Python programming and server frameworks. • Mathematical Foundations: Strong quantitative background with deep knowledge of linear algebra, calculus, probability, statistics, and optimization techniques underlying modern ML algorithms. • Productization Track Record: Demonstrable experience taking models out of research environments (notebooks) and successfully engineering them into commercialized products with high reliability. • ML Application Layer: Strong, demonstrable experience defining model architectures and developing production-grade ML applications. • Cloud Platform Expertise: 5+ years of experience working within Google Cloud Platform (GCP), with foundational knowledge of Google Kubernetes Engine (GKE) for model serving. • Database Management: 5+ years of experience with PostgreSQL, including database security implementations. • Execution: Strong analytical skills and problem-solving abilities, with a proven track record of meeting tight deadlines in a fast-paced environment. Preferred Qualifications • MLOps Lifecycle: Experience with MLOps frameworks, continuous deployment for machine learning models, or model monitoring pipelines. • Alternative Tech Stack: Foundational knowledge of or experience with Elixir/Phoenix is highly ideal.