Job to be done

We’re the best way to get better.

About Buoy Health:

Buoy builds a digital health tool that helps people – from the moment they get sick – start their health care on the right foot. Started by a team of doctors and computer scientists working at the Harvard Innovation Laboratory in Boston MA, Buoy was developed in direct response to the downward spiral we’ve all faced when we attempt to self-diagnose our symptoms online. Buoy leverages artificial intelligence – powered by advanced machine learning and proprietary granular data - to resemble an exchange you would have with your favorite doctor – to provide consumers with a real-time, accurate analysis of their symptoms and help them easily and quickly embark on the right path to getting better. Buoy is based in Boston and was founded in 2014.

About the role:

As a Lead Machine Learning Engineer, your core responsibility is to leverage Buoy data to solve problems intelligently and creatively. You will work with Buoy's product and medical teams to improve the accuracy and efficacy of Buoy's AI. You will apply both classical and modern machine learning techniques. You will formulate hypotheses and design experiments to evaluate them. You will develop, specify, and implement validation methods that scale. You will communicate insights about user behavior, agent behavior, data quality, and data completeness to different teams across the company. To be successful, you must have a keen interest in data-driven decision making and helping people make better decisions about their health.

 Responsibilities:

-- Plan and execute on the delivery of scalable machine learning pipelines to support Buoy's core products

-- Define and instrument processes and best practices for model testability, CI/CD, and active monitoring of model performance 

-- Mentor analysts, data scientists, and other engineers in subjects pertaining to data science and machine learning

-- Work closely with product and clinician teams to plan projects and structure work in an Agile development process 

-- Document ML-based systems and facilitate architectural reviews of projects pertaining to data science and machine learning

-- Identify areas of innovation, formulate research hypotheses, and rapidly prototype new models 

 Minimum Qualifications:

-- BS or graduate degree in Computer Science, Statistics, or related

-- At least 5-7 years professional experience as a machine learning engineer or data scientist 

-- Experience in both rapid prototyping and experimentation, as well as in bringing machine learning models into a production environment

-- Deep knowledge in one or more of the following areas: reinforcement learning, deep learning / artificial neural networks, recommendation systems, or generative adversarial network, and natural language processing

-- Experience with one or more programming languages used for machine learning, preferably Python

-- Experience with cloud computing in AWS, and bonus points for experience with platforms like SageMaker for machine learning and Snowflake for data warehousing

-- Excellent written, spoken, and visual communication skills

Preferred Qualifications:

-- Graduate degree in Computer Science, Statistics, or related

-- Experience in personalization, consumer behavior, HCI, and/or healthcare

-- Experience with PyTorch, SageMaker, or TensorFlow

-- Familiarity with data visualization techniques 

Benefits:

-- Stock Options

-- Unlimited PTO

-- Medical, Dental, Vision

-- 401k with matching

-- Dogs in the office!

-- Half day Fridays 

**This role is located in the United States. Unfortunately, we are unable to support international applicants at this time.