Senior AI/ML Engineer (Magnet-Griffeye)

Who We Are; What We Do; Where We’re Going   Magnet Forensics is a global leader in the development of digital investigative software that acquires, analyzes, and shares evidence from computers, smartphones, tablets, and IoT-related devices. We are continually innovating so our customers can deploy advanced and effective tools to protect their companies, communities, and countries.   Serving thousands of customers globally, our solutions are playing a crucial role in modernizing digital investigations, helping investigators fight crime, protect assets, and guard national security.   With employees based around the world, Magnet Forensics has been expanding our global presence. As a part of Magnet Forensics, you can expect to make a difference in the world, no matter what role you play. You’ll be supported through learning and development, not to mention an incredible team with unbelievable talent and integrity.   If you think you would be the right person to join our team working towards this goal, we would love to hear from you! 

Role Overview 

We are looking for a Senior AI/ML Engineer to join our team, building and operating AI systems that power our digital forensics capabilities; our work ranges from model training and evaluation through deployment, retrieval-augmented generation (RAG) pipelines, and agentic workflows. You will own complex initiatives end-to-end, shipping AI-powered systems that surface critical leads and insights for investigators, helping them solve cases faster and with greater confidence. You'll work closely with Product, UX, and other engineering teams to ensure our models and systems advance what's possible while meeting real-world constraints. 

What You’ll Do

• 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.

What We’re Looking For

• 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.

Nice to Have Skills

• 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 w