Emerald is an MIT spin-off founded by CSAIL faculty and researchers to fundamentally change the way health is monitored in the home. Based in Cambridge, we are looking for motivated technical talent to join our small but high-powered team as we take Emerald to the next level.
A few positions are listed below, but we are always looking for talented individuals.
If that's you, email us at: firstname.lastname@example.org
At Emerald, we are building a full-fledged distributed system consisting of reliable, high performance IoT sensors that collect and process wireless signals, and a cloud infrastructure that securely receives and stores all the data. The cloud also performs advanced AI and machine learning techniques to build accurate and detailed health analytics. As a core team member, the ideal candidate would be multi-talented, excited to tackle hard problems, and deliver at startup pace while working with a small, tight knit group of skilled and experienced engineers.
BS degree in Computer Science.
Masters’, PhD, and/or 1+ years of industry experience in computer science and engineering preferred.
Software development experience in one or more general purpose programming languages like C++ and Python.
Experience working with two or more of the following: Unix/Linux, high performance software systems, scalable distributed systems, front-end frameworks, and machine learning.
Emerald is leveraging a fundamentally new mode of sensing - our custom radio signals - to measure a precise and diverse set of metrics that help understand the health of patients - the speed at which they walk, their sleep, their breathing signals, views of their day to day behavior relevant to various diseases, and much much more. These are challenging machine learning problems, and we are solving them with novel and cutting edge algorithms. As a core team member, the ideal candidate would be a researcher-engineer with a deep understanding of machine learning techniques and the latest research in the area, who simply loves to design and build new models and learning systems with top notch performance on real world data.
PhD in machine learning or computer vision.
Understanding of machine learning and deep learning research.
Experience in building and debugging machine learning and deep learning systems.
Publications in top tier machine learning conferences like CVPR, ICML, and NIPS.
Experience/understanding of server, disk, and network issues involved in building machine learning systems at scale.
1 or more years of industry experience in machine learning.