
Director of Machine Learning (Hybrid)
- Austin, TX
- Permanent
- Full-time
- Lead the end-to-end development and deployment of machine learning and deep learning models-including data acquisition, feature engineering, experimentation, model refresh, and production monitoring.
- Drive the design and implementation of secure, reliable, and scalable ML systems for real-time fraud detection and risk mitigation.
- Partner with Product Management and Engineering teams to define AI/ML strategies, translate business problems into ML solutions, and align on priorities and roadmaps.
- Oversee model performance tracking and lead continuous improvement efforts through rigorous evaluation and iteration.
- Guide architecture and design decisions, incorporating the latest advancements and best practices in AI/ML and software engineering.
- Lead and manage cross-functional Agile teams, ensuring the delivery of impactful and high-quality features.
- Champion a culture of operational excellence by implementing tools, workflows, and best practices that drive productivity and reduce development cycle times.
- Establish and track engineering effectiveness, product quality, and delivery performance metrics-and guide the team to exceed them.
- Stay up to date with the latest developments in AI/ML and help shape the long-term technical strategy.
- Mentor and grow a geographically distributed team of machine learning engineers, data scientists, and software engineers.
- Play a key role in talent acquisition, coaching, and leadership development across the team.
- 10+ years of relevant work experience with a Bachelor's degree, OR
- 13+ years of relevant experience in lieu of a degree
- 12+ years of experience with a Bachelor's degree, OR
- 8-10 years with an Advanced Degree (e.g., MS, MBA), OR
- 6+ years with a PhD in Computer Science, Data Science, Statistics, or a related field
- Proven leadership in delivering real-world AI/ML products at scale, especially in high-stakes or regulated environments
- Deep expertise in ML/DL model development, feature engineering, A/B testing, model deployment, and lifecycle management
- Strong foundation in algorithms, data structures, and problem-solving
- Experience building mission-critical systems that are secure, reliable, and performant
- Demonstrated success in driving Agile delivery, improving engineering metrics, and fostering collaboration across global teams
- Proven ability to hire, develop, and retain top technical talent
- Exceptional communication and stakeholder management skills-able to influence across product, engineering, and executive leadership
- Strategic mindset with passion for innovation and staying ahead of technological trends in ML/AI