We are working on different areas where AI/ML can help portfolio managers, researchers, risk managers, and other functions. Some examples are: enabling traders/researchers to consume vast amounts of research and literature by organizing, summarizing information, sometimes in near real-time; doing quantitative modeling to price instruments, modeling demand and supply of commodities and other fundamentals; modeling weather, optimizing algorithmic execution of trades; modeling and building simulations of risk.

The AI/ML work spans usage of LLMs, deep learning, reinforcement learning and other ML techniques. The work also requires building AI/ML infrastructure for data wrangling, building feature stores, building frameworks for deployments, measurements, re-training, etc.

We are seeking a MLE who has experience with LLMs, deep learning, reinforcement learning as well as an interest and ability to keep up with advancements in AI/ML. The candidate will build workflows for weather modeling (training and inference), and work with internal teams to validate forecasts and integrate with user workflows.

Requirements:

• MS degree (PhD preferred) in computer science, mathematics, computational science/engineering, or related technical field or equivalent experience
• 2+ yrs of relevant experience
• Strong Python programming skills
• Familiarity with containers, numeric libraries, modular software design
• Deep knowledge of state-of-the-art machine learning techniques and algorithms (graph networks, diffusion models, reinforcement learning etc.) with experience in using major deep learning frameworks (PyTorch, JAX, etc.)
• Experience with development and application of machine learning techniques to solve real world problems in geospatial/weather/energy domains
• Strong analytical skills with bias for action
• Good time-management and organization skills to thrive in a fast paced, dynamic environment
• Solid written and oral communications skills. Good teamwork and interpersonal skills

Ways to stand out:

• Experience using multi-node systems with data-parallel and model-parallel programming, and experience with AI/ML model performance optimization
• Experience with simulating physical systems and/or generating large tensors, such as video generation
• Published papers in the field of AI in scientific computing, especially in weather, climate and energy applications
• Familiarity with common tooling in the weather/climate/geospatial ecosystem (xarray, zarr, geopandas)
• Stay up to date with the latest research and innovations in deep learning techniques