Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
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.
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
- 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.
Benefits
- Generous time off policies
- Competitive compensation
- Volunteer opportunities
- Reward and recognition programs
- Employee committees & resource groups
- Healthcare and retirement benefits
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