Machine Learning Engineer
The Machine Learning Engineer will train and fine-tune models, build evaluation suites, analyze model failures, and improve dataset and training quality. The role requires at least two years of ML engineering or applied data science experience, strong Python skills, and practical model evaluation expertise.
About the job
Responsibilities
- Train and fine-tune models using curated datasets; run systematic hyperparameter and architecture experiments.
- Build and maintain evaluation suites, including regression tests for model behavior and benchmark tracking across model versions.
- Analyze model failures, including false positives and false negatives, and translate findings into dataset or training improvements.
- Package models with documentation, benchmarks, and reproducible evaluation results for handoff to the Engine team.
- Contribute to internal tooling for experiment tracking and model comparison.
Requirements
- 2+ years of experience in machine learning engineering or applied data science.
- Strong Python skills and hands-on experience with modern training and fine-tuning workflows.
- Experience with structured model evaluation, including accuracy, precision/recall tradeoffs, drift, and edge-case analysis.
- Ability to work against defined acceptance criteria and production constraints.
Benefits
- Ongoing training and professional development opportunities.
- Collaborative work environment focused on cybersecurity innovation.
- Mission-driven work protecting critical infrastructure and digital assets.
Skills
Python, Machine Learning, Applied Data Science, Model Training, Model Fine-Tuning, Hyperparameter Tuning, Model Evaluation, Regression Testing, Benchmarking, Experiment Tracking, Data Quality, Dataset Pipelines
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