Senior Machine Learning Engineer
Magnet Forensics
What You’ll Do
- 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.
What We’re Looking For
- 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;
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience in applied ML research and engineering.
Nice to Have Skills
- 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.)
