Senior Machine Learning Engineer (on-site)

OPSWAT, a global leader in IT, OT, and ICS critical infrastructure cybersecurity, delivers an end-to-end platform that gives public and private sector organizations and enterprises the critical advantage needed to protect their complex networks, secure their devices, and ensure compliance. Over the last 20 years our commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions globally, solidifying our role in protecting the world’s critical infrastructure and securing our way of life. 

What You Will Be Doing 

• Lead the training, testing, and optimization of ML models for cybersecurity and malware, owning technical decisions end to end.
• Evaluate and select model architectures across classical ML and deep learning, setting the standard for how the team approaches new problems.
• Drive data exploration, feature engineering, and preprocessing across large, structured and unstructured datasets.
• Design and own robust model evaluation pipelines—validation strategies, benchmarking, and performance analysis—and define the best practices others follow.
• Partner with data engineers and threat researchers to raise data quality, feature sets, and model performance.
• Own the deployment, integration, and monitoring of ML models in production, ensuring reliability at scale.
• Define and improve scalable ML workflows and model development lifecycle practices across the team.
• Work with cross-functional teams to translate ambiguous business and product requirements into ML strategy and solutions.
• Mentor junior and mid-level engineers, sharing expertise and raising the technical bar.
• Influence research and product direction for ML in critical-infrastructure security.

What We Need From You 

• Degree in Computer Science, Mathematics, Engineering, AI, or a related field (advanced degree a plus).
• 5+ years of hands-on machine learning experience, including a track record of shipping models to production.
• Deep understanding of ML algorithms, model training, evaluation techniques, and optimization methods.
• Strong Python skills and expertise with common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Proven experience with data exploration, feature engineering, and working with real-world datasets at scale.
• Strong command of model evaluation metrics and validation strategies.
• Solid experience deploying and integrating ML models into production systems.
• Experience with cloud platforms (AWS preferred).
• Familiarity with MLOps practices and tools.
• Demonstrated technical leadership: mentoring, driving architecture decisions, and setting standards.
• Experience in cybersecurity, malware analysis, or a related security domain is a strong plus.
• Strong problem-solving skills, attention to detail, and a bias toward experimentation.
• Excellent communication and the ability to lead cross-functional collaboration.