As a Senior Machine Learning Engineer, you will be responsible for tackling a wide range of problems from natural language processing to time series prediction. You are expected to move beyond the theoretical confines of academia and apply your tradecraft to build machine learning systems on real world data. In this role you will constantly collaborate and work with groups of engineers. The machine learning team is responsible for delivering ML-based solutions from problem framing, to prototyping, to production application.
You will be responsible for designing and implementing solutions in support of indices business, leveraging developments and data from across the organization, and contributing to the advancement of machine learning.
Here are some of your significant tasks:
● Conduct original research on large proprietary and open source data sets
● Identify, research, prototype, and build predictive products
● Build cutting-edge models for understanding vast amounts of textual data
● Write production code
● Write tests to ensure the robustness and reliability of your productionized models
● Work closely with software engineers to build incredible systems
● Guide and mentor team members and help educate the business on the art of the possible
We are looking for applicants with:
● Highly creative and technically capable with an interest in shaping a growing team
● Strong expertise in Python and have shipped production-ready code
● Comfortable with wrangling structured and unstructured data and have opinions on how to get the data right
● Experience with driving the design and implementation of advanced machine learning methods -- especially modern NLP techniques.
● Strong statistical knowledge, intuition, and experience modeling real data
● Detail orientedness
● Stellar ability to communicate even the most complicated methods and results to a broad, often non-technical audience
● Effective coding, documentation, and communication habits
● Several of the following terms should hold deep meaning for you: LSTM, lookahead bias, bagging, boosting, stacking, information retrieval, batch norm, entity recognition, bootstrapping, Glorot initialization, Kullback-Leibler divergence, GLOVE, SMAPE, HMM, MAP, exponential family, VC dimension, EM, L1, TD(Lambda)
● A github/kaggle profile shows a project or problems you’ve tackled
● Experience or a strong interest in finance Qualifications:
● 5+ years of experience being a major machine learning contributor at a top company, hedge fund, or university
● 10+ years development experience in a senior capacity, working self-directed and providing mentorship to junior members of staff
● Experience writing about machine learning, either academically or casually
Technologies You Will Use
● Python -- specifically Numpy, SciPy, Pandas, scikit-learn, LightGBM, XGBoost, and other packages to support the ML development lifecycle
● Deep learning packages like TensorFlow and PyTorch
● MLOps platforms like MLFlow and Weights & Balances