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Team Leader ML Engineer

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.
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