Machine Learning Engineer

<h3>📋 Description</h3> • We are seeking an experienced and pragmatic Machine Learning Engineer to join our growing Cloud Operations team. • This role will focus on building and maintaining the pipelines and infrastructure that make machine learning and analytics possible in a security-focused, production-grade environment. • As a core contributor, you will work closely with data scientists, data engineers, and platform engineers to deploy and monitor ML models, build reusable pipelines, manage feature stores, and ensure robust, scalable, and secure data operations for our cybersecurity platform. • You’ll play a key role in operationalizing machine learning in a way that directly enhances our AI-powered pentesting and analytics capabilities. • Design, build, and maintain reliable MLOps pipelines that support versioned, testable, and reproducible model training and deployment. • Develop CI/CD pipelines for model promotion, validation, canary testing, and rollback. • Automate model performance monitoring, logging, and alerting to maintain model health in production. • Collaborate with Data Engineering and Data Science teams to build and maintain data pipelines, feature stores, and high-quality training datasets. • Support the creation of ML-friendly data assets that meet latency, freshness, and accuracy requirements. • Integrate robust data validation, lineage tracking, and quality checks throughout the pipeline. • Define and manage scalable infrastructure for model training and inference using container orchestration platforms (e.g., Kubernetes). • Apply infrastructure-as-code (IaC) principles to build reproducible environments for experimentation and production. • Ensure compliance with security and privacy best practices in model and data handling. • Work side-by-side with data scientists to enable fast experimentation while maintaining production-grade standards. • Facilitate efficient use of GPU/TPU resources, experiment tracking tools, and model registries. • Participate in planning, postmortems, and optimization of our ML platform to improve velocity and reliability. <h3>🎯 Requirements</h3> • 3+ years of experience in software engineering or DevOps roles, with 1+ years focused on MLOps or ML infrastructure. • Strong background in deploying machine learning models to production, including model versioning, rollback, and performance tracking. • Advanced proficiency in Python, including common ML libraries (e.g., scikit-learn, MLflow, PyTorch, TensorFlow). • Strong skills in building and maintaining data pipelines using tools like Apache Airflow, dbt, or similar. • Experience working with cloud platforms (preferably GCP) and infrastructure tools like Docker, Kubernetes, Terraform, or Pulumi. • Solid understanding of data engineering concepts such as batch and streaming ETL, data partitioning, and schema evolution. • Familiarity with cybersecurity, penetration testing workflows, or secure data handling practices is a plus. • Comfort working in an agile, fast-paced, and mission-driven startup environment <h3>🏖️ Benefits</h3> • Earn competitive compensation and an attractive equity plan • Save for the future with a 401(k) program (US) • Benefit from medical, dental, vision and life insurance (US) • Leverage stipends for: • Wellness • Work-from-home equipment & wifi • Learning & development • Make the most of our flexible, generous paid time off and paid parental leave