Senior AI/ML Engineer (Magnet-Griffeye)

Own complex AI/ML initiatives from ideation through experimentation, evaluation, deployment, and handoff for integration;

Design and prototype agentic workflows where models reason, plan, call tools, and collaborate with other systems to accomplish complex tasks;

Train, fine-tune, or adapt models when the problem demands it, and know when a well-designed system beats a bigger model;

Work with complex, real-world datasets, developing pre-processing, augmentation, and evaluation techniques that enhance model quality and fairness;

Collaborate cross-functionally with other engineering teams to ensure models and systems are production-ready, observable, scalable, and meet real user needs;

Contribute to reusable engineering infrastructure that accelerates experimentation, evaluation, and deployment;

Embed ethical, responsible, and secure AI practices into design, evaluation, and deployment decisions, raising concerns early when they surface;

Mentor other engineers at different levels on experimental design, evaluation methodology, and technical decision-making. Helping the team to level up by establishing patterns and best practices.

5+ years of professional experience in ML or applied AI (or equivalent depth demonstrated through delivered work), with a track record of delivering models or AI systems into production;

Demonstrated depth in at least one of: model training and evaluation, agentic system design, or retrieval and evaluation architecture, with working familiarity across the other areas;

Experience evaluating ML/AI systems. Designing representative evaluation distributions, checking that training signals or metrics reflect the actual outcome you care about;

Comfortable working with large, complex, and/or unstructured datasets, with a strong understanding of trade-offs between model quality, cost, inference speed, and system complexity;

Proficiency in Python and working fluency with modern ML/AI frameworks and tooling (e.g., PyTorch, inference servers, LLM/agent frameworks);

Strong communication and cross-functional collaboration skills; comfortable working with Engineers, Researchers, Product, and Design;

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.

Familiarity with vector databases, embedding models, and context retrieval strategies;

Background in NLP, computer vision, or other relevant ML domains;

Familiarity with MLOps tooling (e.g., experiment tracking, model versioning, CI/CD for ML);

Contributions to open-source ML/AI projects or publications in peer-reviewed venues;

Experience working with cloud providers like AWS or Azure; or other relevant production AI/ML infrastructure;

Experience working with AI tools as part of your development workflow (e.g., Claude, GitHub Copilot, etc.)