Manager - Machine Learning Engineer

Job Summary

As the Machine Learning Manager at Traceable by Harness, you will play a pivotal role in leading and scaling our ML initiatives from prototype to production. You will guide a team of machine learning engineers, and data scientists to design, develop, and deploy high-performing ML solutions that power our core products. This role demands a balance of technical expertise, strategic planning, and people leadership to ensure that our ML models operate reliably, deliver measurable impact, and evolve continuously in production environments.

• Lead and grow a high-performing Machine Learning team, providing technical guidance, mentorship, and career development.
• Define and execute the ML strategy aligned with Traceable’s overall product and business goals.
• Oversee the full ML lifecycle - from problem formulation, data acquisition, feature engineering, modeling, training, evaluation, to production deployment and monitoring.
• Work closely with Security Research and Engineering teams to incorporate domain-specific knowledge into models for API threat detection, anomaly detection, user behavior modeling, and more.
• Ensure models are robust, explainable, and optimized for performance and scalability in production.
• Stay abreast of the latest research and industry trends in ML, security, and anomaly detection, bringing innovative techniques to the team.
• Collaborate with Product Management to translate customer needs into ML features and capabilities.
• Foster a culture of experimentation, engineering excellence, and continuous learning

Requirements

• 8+ years of experience building and deploying production-grade machine learning systems, with at least 2+ years in a leadership or management capacity.
• Proven experience designing and implementing ML solutions for security, anomaly detection, fraud detection, or related fields.
• Strong programming skills in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Deep understanding of data pipelines, feature engineering, model evaluation, and MLOps best practices.
• Experience with large-scale data processing and distributed systems.
• Ability to work cross-functionally and communicate complex concepts to non-ML stakeholders.
• Strong people leadership skills, with a track record of hiring, developing, and retaining top ML talent.

Nice-to-Haves

• Experience working in API security, application security, or broader cybersecurity domains.
• Familiarity with monitoring and observability tools such as Prometheus, Grafana, or building custom solutions for detecting model drift and data quality issues.
• Hands-on experience with feature stores, model versioning, and best practices for managing ML assets in production.

What We Offer

• Competitive salary and benefits package.
• A supportive and dynamic work environment with a strong focus on professional growth.
• Opportunities to work with state-of-the-art technology and shape the future of API security.