Senior Machine Learning Scientist


New York, NY, USA Remote

Full time

Nov 22

This job is no longer accepting applications.

At Compass, we envision a world where the experience of selling or buying a home is simple and pleasant for everyone. Founded in 2012, Compass provides an end-to-end platform that empowers residential real estate agents to deliver exceptional service to their seller and buyer clients, all in service of our mission to help everyone find their place in the world.

Senior Machine Learning Scientist


Compass is building the first modern end-to-end real estate platform by integrating agents, buyers and sellers through technology. Before Compass, no one has achieved the blend of the Natural Intelligence that hundreds of thousands of enterprising real estate agents bring to this market, with the Artificial Intelligence that cloud, mobile and AI technologies enable.


As one of the fastest growing technology companies of our generation, in an industry larger than any other, we have an opportunity and obligation to build a world-class engineering team and the operating platform that will transform the real estate industry. In 2019 we tripled the size of our Product & Engineering team, and are searching for creative and inspiring colleagues at all levels of the engineering organization to join us as we continue to expand in 2020.

About the Role:

Compass is looking for a Senior Machine Learning Scientist to help us build the future of real estate. Compass is building an end-to-end real estate technology platform that empowers agents and consumers. The AI team is responsible for using machine learning to build features which empower our agents through intelligent tools, and add intelligence to our consumer facing products. We are looking for someone with a proven track record of deploying and supporting machine learning models at scale in customer facing features.


Using your machine learning skills to explore and deeply understand Compass’s data.

Architect machine learning products to solve business needs

Perform feature engineering and modelling tasks on top of this data to power customer facing features. 

Collaborate with machine learning engineers to produce features in a production ready state. 

Support machine learning features throughout the product life cycle.

Drive design and implementation of our in-house machine learning infrastructure.


What we look for:

Experience working on machine learning in a product oriented environment.

Ability to collaborate with scientists, product management and work with an engineering-focused, iterative team to build and establish product requirements.

Knowledge of machine learning frameworks and tooling, for example Spark, PyTorch, SciKit learn, etc. 

Comfortable building prototypes from scratch

Experience working with a microservice based architecture.

Experience with AWS development.

BS or MS in Computer Science/Machine Learning or equivalent

5+ years experience is preferred.

At Compass, our mission is to help everyone find their place in the world. This means we continually celebrate the diverse community different individuals cultivate. As an equal opportunity employer, we stay true to our mission by ensuring that our place can be anyone’s place.

Check out our Engineering blog!

Do your best work, be your authentic self.


At Compass, we believe that everyone deserves to find their place in the world — a place where they feel like they belong, where they can be their authentic selves, where they can thrive. Our collaborative, energetic culture is grounded in our Compass Entrepreneurship Principles and our commitment to diversity, equity, inclusion, growth and mobility. As an equal opportunity employer, we offer competitive compensation packages, robust benefits and professional growth opportunities aimed at helping to improve our employees' lives and careers.

Notice for California Applicants

You must be logged in to to apply to this job.


Your application has been successfully submitted.

Please fix the errors below and resubmit.

Something went wrong. Please try again later or contact us.

Personal Information


View resume