About the Role
In this role, you will design, build, and improve machine learning models for cybersecurity and malware-related use cases. You will focus on experimenting with model architectures, preparing and evaluating data, and supporting the deployment and integration of ML solutions into production systems. You will work closely with engineers and product teams to ensure models are robust, scalable, and impactful.
You Will Have the Opportunity to
- Train, test, and optimize machine learning models for cybersecurity and malware detection use cases.
- Experiment with and evaluate different model architectures, including classical ML and deep learning approaches.
- Perform data exploration, feature engineering, and data preprocessing on large, complex datasets (structured and unstructured).
- Design and execute model evaluation pipelines, including performance analysis, validation strategies, and benchmarking.
- Collaborate with data engineers and threat researchers to improve data quality, feature sets, and model performance.
- Support the deployment, integration, and monitoring of ML models in production environments.
- Contribute to building scalable ML workflows and improving model development lifecycle practices.
- Work with cross-functional teams to translate business and product requirements into ML solutions.
What We Are Looking For
- Degree in Computer Science, Mathematics, Engineering, AI, or a related field.
- Solid understanding of machine learning algorithms, model training, evaluation techniques, and optimization methods.
- Hands-on experience with Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with data exploration, feature engineering, and working with real-world datasets.
- Familiarity with model evaluation metrics and validation strategies.
- Basic understanding of deploying and integrating ML models into production systems.
- Experience with cloud platforms (AWS preferred) is a plus.
- Familiarity with MLOps practices and tools is an advantage.
- Strong problem-solving skills, attention to detail, and willingness to experiment and learn.
- Ability to communicate findings and collaborate effectively within cross-functional teams.