ML Backend Team Leader

Position Summary: As a Team Leader at DeepKeep.ai, you will play a pivotal role in leading our backend developers and ML engineers, and bridging between them and our research team. This hands-on role demands a deep understanding of machine learning technologies.

Key Responsibilities:

• Lead the development of our backend and firewall platform, implementing efficient and robust solutions in production.
• Supervise the translation of advanced research prototypes into scalable, production-grade software.
• Collaborate closely with data scientists to understand research findings and translate them into practical, scalable solutions.
• Design and implement efficient machine learning systems compatible with diverse data types and integrable with technologies like transformers.
• Drive ambitious projects through collaboration with cross-functional teams, ensuring seamless integration of machine learning technologies across our product suite.

Who we're looking for:

• A visionary leader with substantial experience in software engineering and machine learning development.
• A go-getter who knows how to get things done while making sure their team members are motivated and evolving as developers.
• Ability to translate intricate, algorithmic prototypes into scalable, market-ready solutions.
• Expertise in specialized machine learning databases and optimizing the performance of ML applications.
• A collaborative team player known for effective teamwork, knowledge sharing, and thriving in dynamic environments.

Qualifications:

• Minimum 6 years of development (or lead) experience
• Of those, at least 2 years experience as a machine learning engineer or a similar role developing data-driven software.
• Exceptional Python coding skills, including experience with APIs, Kafka, SQL, NoSQL, and other relevant technologies. Knowledge of strictly typed languages is advantageous.
• Demonstrated leadership in managing end-to-end ML projects (pipelines, models, datasets, backend).
• Proficiency in machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and data processing libraries (e.g., NumPy, Pandas).
• Strong problem-solving and critical thinking skills.
• Familiarity with ML databases and vector databases is advantageous.
• Solid foundation in MLOps, with hands-on experience in designing and managing large-scale ML infrastructures.
• Understanding of statistical concepts and algorithms used in machine learning.