Senior AI/ML Engineer (Magnet-Griffeye)

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. 

The work on our team is diverse. In a given quarter, you might design an evaluation harness for an agentic system, fine-tune a model, prototype a retrieval pipeline, or partner with other engineering teams on production scaling. You'll work across the full stack (training, evaluation, RAG, agentic systems, deployment, and scale), and you'll be trusted to pick the right tool for each problem. From building AI that investigators can trust to turn months of case work into days, to figuring out what 'good' looks like when users are searching for a needle in a haystack they've never seen before, our team works on challenging and meaningful problems. If these are the kinds of problems that interest you, you will feel at home on this team! 

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 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.)