Sr Machine Learning Engineer, AI Research

You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems. You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.

• 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

• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)

• 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