Sr Machine Learning Engineer, AI Research

Cribl Cribl · Enterprise · CA · Engineering

This role focuses on designing, training, and evaluating machine learning models, including language models, and translating academic research into production-ready systems within an enterprise AI context. The role involves building ML pipelines, optimizing model performance, and staying current with AI/ML research.

What you'd actually do

  1. Design, train, and evaluate machine learning models across a range of research and applied AI initiatives
  2. Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements
  3. Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems
  4. Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation
  5. Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation

Skills

Required

  • Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ 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

Nice to have

  • Master's or PhD a plus

What the JD emphasized

  • Deep hands-on experience training and evaluating ML models, including language models
  • Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques

Other signals

  • design, train, and evaluate machine learning models
  • translate cutting-edge academic advances into practical, production-ready systems
  • Build and maintain robust ML pipelines