Senior Machine Learning Engineer

Magnet Forensics

58d•$125,000 - $175,000•Hybrid

About The Position

We are looking for a Senior Machine Learning Engineer to join our team, designing, experimenting with, and optimizing applied ML and AI systems that power our digital forensics capabilities. You will lead the development of new models, training techniques, evaluation methods, and AI-powered systems that surface critical leads and insights for investigators, helping them solve cases faster and with greater confidence. As part of this team, youâll work closely with Product, UX, and our Brain team to ensure our models and systems advance whatâs possible while meeting real-world constraints. Youâll own complex initiatives end-to-end, including ideation and experimentation, to evaluation and handoff for integration, working with our team to advance the state-of-the-art in digital forensics.

Requirements

5+ years of professional experience in machine learning or applied AI, with a track record of delivering models into production or production-ready pipelines
Strong Python programming skills, with experience in building maintainable, scalable ML systems
Experience designing and running experiments, selecting appropriate metrics, and evaluating models
Practical experience working with large language models in production or research prototypes, including prompt engineering, fine-tuning or adaptation, and/or retrieval-augmented generation
Hands-on experience with deep learning frameworks (eg, PyTorch, TensorFlow) and deployment frameworks (eg, Triton, TorchServer)
Experience working with large, complex, and/or unstructured datasets, with a strong understanding of trade-offs between model quality, cost, inference speed, and system complexity
Ability to work cross-functionally with engineers, researchers, product managers, and designers
Strong communication skills for both technical and non-technical audiences
Bachelorâs or Masterâs degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience in applied ML research and engineering.

Nice To Haves

Experience with agentic systems, tool calling, multi-step reasoning workflows, or LLM evaluation frameworks
Familiarity with vector databases, embedding models, and context retrieval strategies
Background in NLP, computer vision, or other relevant ML domains
Familiarity with MLOps tooling (eg, experiment tracking, model versioning, CI/CD for ML)
Contributions to open-source ML projects or publications in peer-reviewed venues
Experience working with cloud providers like AWS or Azure
Experience working with AI tools as part of your development workflow (eg, Claude, GitHub Copilot, etc.)

Responsibilities

Design, implement, and evaluate state-of-the-art ML/AI models and systems
Lead experiments, define success metrics, build evaluations, and iterate to improve performance, efficiency, and reliability
Collect, build, and work with complex, real-world datasets, developing preprocessing, augmentation, and feature engineering techniques that enhance model training and fairness
Design and prototype agentic workflows where models reason, plan, call tools, and collaborate with other systems to accomplish complex tasks
Collaborate cross-functionally with our Brain team to ensure models are production-ready, observable, scalable, and meet real user needs
Stay at the forefront of ML/AI research, assessing new techniques, frameworks, and trends, and translating them into practical innovations for our products
Contribute to building reusable research infrastructure and tooling that accelerates experimentation and improves reproducibility
Ensure ethical, responsible, and secure AI practices are integrated into model design, training, and evaluation
Mentor other engineers on ML and AI best practices, experimental design, evaluation methodology, and technical decision-making.