The ML Platform is a critical component in increasing revenue at our firm. The role will
require everything from high-level architecture design to performant implementation.
Responsibilities include: • Build distributed systems supporting complex modeling over time series data • Lead design, development, and deployment of machine learning systems • Understand and translate modeling goals & requirements into an engineering roadmap; future responsibilities could include leading key engineering initiatives in the forecasting platform • Work closely with business side • Review code developed by other developers and provide feedback to ensure best
practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)
Qualifications • Industry experience with modern ML infrastructure, ML libraries, and ML ops • Have built large scale machine learning models and systems • Familiarity with the fundamentals of probability, • Master's degree in Computer Science, Data Science, Machine Learning, or a related field. Ph.D. is a plus • Fluency in Python with the ability to write clean, modular, well-documented code as well as a solid understanding of coding best practices • 5+ years in industry developing and deploying machine learning models, preferably in NLP, LLM and ideally generative AI domains • Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Transformers • Familiarity with cloud platforms (AWS) and containerization technologies (Docker)