Machine Learning Engineer


Cultivate is a people analytics platform that empowers managers to optimize productivity and retain top talent.  We provide real-time and predictive analytics for companies and managers in areas of engagement analysis, retention risk, and resource alignment.  Our mission is to help companies provide their employees a more engaging, fulfilling, and productive workplace by continuously improving the manager-employee relationship. 

We are a small founding team that is well funded by Samsung NEXT.  Our San Francisco office is a co-working space with a great sense of community, and views of the Bay.  We have access to amenities like a fully stocked kitchen and all the computer equipment and gadgets you need.  We also have the autonomy of a small, driven startup team; for example, we value our work-life balance and don’t track or limit vacation/sick days.




You’ll be leveraging machine learning and NLP techniques on corporate communications data (calendar events, email & slack messages) to quantify the collaborative dynamics of professional teams and generate predictive analytics for improving manager/employee relationships.  Responsibilities include:

  • Define and implement innovative algorithms for extracting semantic information (e.g. politeness, autonomy, intent) from messages
  • Model the collaboration patterns of teams, identifying predictive signals within daily digital conversations to improve employee satisfaction and improve retention.
  • Roll up your sleeves and dig into the code, developing promising approaches from prototyping to production.
  • Be an influential voice on a small, rapidly innovating team building a next generation product.
  • Set strategic vision and deal with the complexities of an evolving machine learning stack (e.g. data storage, feature engineering, deployment of models)




  • MS or PhD in Computer Science or related quantitative disciplines.
  • Proficient in Python and SQL, with strong computer science fundamentals (algorithms and data structures). Capable of writing high performance, scalable, production level code. 
  • Knowledge of NLP and the semantic interpretation of messages: entity extraction, knowledge graph creation, text classification, and topic modeling.
  • Deep understanding of machine learning techniques, including supervised and unsupervised algorithms, clustering, graph analytics, and time series analysis. Experience with data science toolkits such as numpy, pandas, scikit-learn, tensorflow, etc.
  • Experience with distributed machine learning frameworks (e.g. Spark, GraphX) a plus.
  • Thrives in a fast-moving startup environment, has strong communication skills and is excited about the opportunity to influence product development and roadmapping.

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