Machine Learning Engineer

  • Kensho
  • Cambridge, MA, USA
  • Jul 19, 2018
Full time Developer

Job Description

We are looking for a talented and creative individual to join our team of Machine Learning Engineers. As a ML Engineer at Kensho, you will tackle a wide range of problems from timeseries prediction to natural language processing and are passionate about building machine learning systems on real world data. Do you have extensive experience applying a range of ML models to a diverse set of problems? Do you enjoy moving beyond the theoretic confines of academia to apply your tradecraft in the real world? Does producing data-driven products that will empower decision makers at all levels of the global banking industry and beyond excite you? If so, we want to hear from you.
We take pride in our team-based, tightly-knit startup community that provides our employees with an environment to bring transparency to the biggest challenges in data.

What You’ll Do:

    • 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-ready code
    • Write tests to ensure the robustness and reliability of your productionized models

What We Look For:

    • At least one core programming expertise, such as python (NumPy, SciPy, Pandas), MATLAB, or R
    • Experience with advanced machine learning methods
    • Strong statistical knowledge, intuition, and experience applying machine learning models to real data
    • Stellar ability to communicate even the most complicated methods and results to a broad, often non-technical audience
    • Effective coding, documentation, and communication habits
    • Ability and credibility to direct a team
    • Several of the following terms should hold deep meaning for you: lookahead bias, bagging, boosting, stacking, information retrieval, entity recognition, bootstrapping, LSTM, Glorot initialization, Kullback-Leibler divergence, GLOVE, SMAPE, HMM, MAP, exponential family, VC dimension, EM, L1, TD(Lambda)

How to Really Get Our Attention:

    • 3+ years of experience being a major machine learning contributor at a top company, hedge fund, or university
    • Your github/kaggle profile showing a project or problems you’ve tackled

Technologies We Like:

      • Python and specifically Numpy, SciPy, Pandas, scikit-learn
      • Neural network packages like TensorFlow and Torch
      • ML packages like LightGBM and XGBoost


    • Medical, Dental, and Vision insurance with 100% premium covered
    • Unlimited vacation days
    • Paid Parental Leave
    • 401(k) plan with employer match
    • Free snacks and drinks
    • Dog-friendly office
    • Cardio machines and weights in the office
    • Hubway (bike sharing program) membership