What the Candidate Will Need:- Technical Leadership: Demonstrated ability to lead and manage high-performing engineering teams, particularly in the ML and AI domain.
- Hands-on Engineering Background: A strong foundation in software engineering, with a track record of building and deploying robust, scalable systems.
- ML and AI Expertise: Proficiency with machine learning frameworks such as TensorFlow, PyTorch, or similar, and a deep understanding of the ML lifecycle.
- Cross-functional Collaboration: Proven ability to work cross-functionally, effectively communicating and advocating for the team across different departments.
- Roadmap Execution: A solid history of executing complex roadmaps, delivering projects on time, and maintaining high-quality standards.
Bonus Points:- Advanced ML/AI Experience: Experience with advanced ML/AI techniques, such as deep learning or reinforcement learning.
- Experience with High-Performance Computing: Expertise in optimizing computing resources for ML workloads, including experience with GPU/TPU and other specialized hardware.
- Open Source Contributions: Active involvement in the ML or software engineering community through open source contributions.
- Data Science Dependency Management: Experience in managing dependencies in complex data science projects.
- Problem-Solving Prowess: A systematic approach to solving complex technical problems, with a deep understanding of algorithms and data structures.
* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .