Senior ML Engineer, MLOps & Mechanistic Interpretability

What you'll do

• Build the MLOps foundation and infrastructure for scalable Transformer-based model deployment.
• Develop "glass box" tooling to transform model internals into human-understandable, regulatory-compliant insights.
• Map latent embeddings to human-readable credit concepts using Mechanistic Interpretability (XAI) research.
• Design systems of systems for training and running machine learning models following industry best practices.
• Lead a team of engineers by defining project scopes, providing guidance, and mentoring colleagues.
• Manage the end-to-end development lifecycle from initial analysis and design to successful deployment.
• Identify and report intellectual property through patent applications, white papers, and technical presentations.
• Define the technical vision and roadmap for machine learning architectures aligned with organizational goals.

What we're looking for

• Bachelor's degree in a STEM major or equivalent experience required.
• Master's degree and AI/ML coursework (preferred).
• 7+ years of related work experience, including proven experience leading teams of MLE, DS, SDE, or DevOps roles.
• Experience with end-to-end development of ML models from ideation to deployment ensuring scalability and reliability.
• Ability to review code for quality and performance while establishing SDLC best practices (preferred).
• Ability to design scalable machine learning architectures and define technical roadmaps (preferred).
• Proficiency in data architecture, orchestration, ETL development, and cloud computing (preferred).
• Ability to read and comprehend research papers in the latest machine learning fields (preferred).
• Cloud Certification (strongly preferred).