Staff Machine Learning Engineer, AI Research

Cribl is a remote-first company focused on empowering employees and delivering innovative AI-enabled Security/Observability platforms. The Staff Machine Learning Engineer will work closely with the founding team and engineers to develop and integrate AI/ML technologies, design and evaluate machine learning models, and collaborate on product development to address customer needs.

Responsibilities:

• Design, train, and evaluate machine learning models across a range of research and applied AI initiatives
• Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements
• Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems
• Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation
• Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation
• Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team
• Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them
• This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones

Requirements:

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 5+ years of industry or research experience
• Deep hands-on experience training and evaluating ML models, including language models
• Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights & Biases, Kubeflow, or similar)
• Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques
• Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize
• Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders
• Master's or PhD a plus