AI/ML Architect

• Design and develop an ensemble of classical and deep learning algorithms for modeling complex interactions between people, software, infrastructure and policies in an enterprise environment
• Design and implement algorithms for statistical modeling of enterprise cybersecurity risk
• Apply data-mining, AI and graph analysis techniques to address a variety of problems including modeling, relevance and recommendation
• Build production quality solutions that balance complexity and performance
• Participate in the engineering life-cycle at Balbix, including designing high quality ML infrastructure and data pipelines, writing production code, conducting code reviews and working alongside our infrastructure and reliability teams
• Drive the architecture and the usage of open source software library for numerical computation such as TensorFlow, PyTorch, and ScikitLearn

You Are

• Able to take on very complex problems, learn quickly, iterate, and persevere towards a robust solution
• Product-focused and passionate about building truly usable systems
• Collaborative and comfortable working across teams including data engineering, front end, product management, and DevOps
• Responsible and like to take ownership of challenging problems
• A good communicator, and facilitate teamwork via good documentation practices
• Comfortable with ambiguity and thrive in designing algorithms for evolving needs
• Intuitive in using the right type of models to address different product needs
• Curious about the world and your profession, constant learner

You Have

• A Ph.D. in Computer Science and Engineering or related field
• 3+ years of experience in the field of Machine Learning, programming in Python, and building scalable distributed systems
• Foundational knowledge of probability, statistics and linear algebra
• Expertise in state-of-the-art AI algorithms such as deep-learning, NLP, probabilistic graphical models, graphical algorithms, reinforcement learning and time-series analysis
• Knowledge of statistical analysis and modeling techniques, and model explainability