About the role

Senior Machine Learning Engineer designing and optimizing ML/AI systems for digital forensic tools. Collaborating with cross-functional teams to lead initiatives and drive innovation in digital investigations.

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.

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.