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