Senior Machine Learning Engineer

Job Title:

We also recognize the importance of closing the 4-million-person cybersecurity talent gap. We aim to create a home for anyone seeking a meaningful future in cybersecurity and look for candidates across industries to join us in soulful work. More at.

Role Overview:

About the role:

• As a Senior Machine Learning Engineer, you will be part of a high-impact team reporting to the Director of Data Science, focused on developing scalable, production-grade machine learning solutions to power cybersecurity products.
• Collaborate with data scientists and engineering teams to build and scale machine learning pipelines.
• Develop and deploy production-ready ML systems for use cases such as:Threat and attack detection,Threat hunting,Malware detection,Anomaly detection
• Build and optimize distributed data pipelines using Apache Spark and Python.
• Design and implement MLOps workflows including data preprocessing, model training, evaluation, and deployment.
• Monitor and retrain ML models based on feedback and performance in production.
• Develop model serving infrastructure for high availability, low latency, and scalability.
• Automate and streamline feature engineering, hyperparameter tuning, and CI/CD pipelines for ML models.
• Ensure model integration into security products with adherence to compliance and data governance standards.

About you:

You are an experienced ML Engineer with a strong foundation in software engineering and a passion for solving complex problems in the cybersecurity domain.

Must-Have Qualifications:

• A Master Degree or Equivalent degree in Machine Learning, Computer Science, or Electrical Engineering, with Mathematics/Statistics at undergraduate level
• 4-6 years of industry experience as a Machine Learning Engineer
• Advanced proficiency in Python and strong programming fundamentals
• Hands-on experience with Apache Spark or PySpark for distributed data processing
• Solid understanding of machine learning concepts and experience with ML libraries like scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience in building, deploying, and monitoring end-to-end ML systems in production
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker Pipelines).
• Proficiency in SQL and handling large-scale data
• Experience working with public cloud platforms (AWS, GCP, or Azure).
• Strong software engineering practices: Git, containerisation (Docker), testing, CI/CD

Good to Have:

• Knowledge of Large Language Models (LLMs) such as GPT, BERT, LLaMA, or Falcon.
• Experience with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)