Senior AIML 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;

Bachelors or Masters 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.)