ML Engineer

Millennium•New York, NY

About The Position

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

Nice To Haves

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