Key Responsibilities:

• Lead the translation of advanced research prototypes into scalable, production-grade software.
• Optimize the utilization of machine learning models, implementing techniques such as early stopping and optimization against adversarial attacks.
• 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.
• Ability to translate intricate, algorithmic prototypes into scalable, market-ready solutions.
• Proficiency in machine learning frameworks (e.g., PyTorch, TensorFlow) and adapting them for modern computing environments (GPU, distributed computing).
• Expertise in specialized machine learning databases and optimizing the performance of ML applications.
• Hands-on experience in MLOps, capable of architecting and overseeing large-scale ML systems.
• Strong grasp of statistical concepts and algorithms essential in machine learning.
• A collaborative team player known for effective teamwork, knowledge sharing, and thriving in dynamic environments.

Qualifications:

• Minimum 6 years of development experience, with at least two years as a machine learning engineer.
• 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.