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)