Algorithms Engineer Intern

Beacon Biosignals is on a mission to revolutionize precision medicine for the brain. We are the leading at-home EEG platform supporting clinical development of novel therapeutics for neurological, psychiatric, and sleep disorders. Our FDA 510(k)-cleared Dreem EEG headband and AI algorithms enable quantitative biomarker discovery and implementation. Beacon’s Clinico-EEG database contains EEG data from nearly 100,000 patients, and our cloud-native analytics platform powers large-scale RWD/RWE retrospective and predictive studies. Beacon Biosignals is changing the way that patients are treated for any disorder that affects brain physiology. 

Beacon Biosignals is seeking an Algorithm engineer intern! You'll work alongside fellow data scientists, neuroscientists, engineers, and clinicians as part of Beacon's Analytics and Machine Learning domain to help us improve our machine and deep learning models. You will benefit from the experience and mentorship of our expert machine-learning team. At Beacon, we've found that cultural and scientific impact is driven most by those who lead by example. As such, we're always seeking out new contributors whose work demonstrates innate curiosity, a bias toward simplicity, an eye for composability, a self-service mindset, and—most of all—a deep empathy toward colleagues, stakeholders, users, and patients. We believe a diverse team builds more robust systems and achieves higher impact. The internship for this position will be located in our Paris office. Beacon practices robust asynchronous work practices, and you'll also be working with remote colleagues and those at our in-person office hubs in Boston and New York City.

Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we also have in-person office hubs available located in Boston, New York and Paris.

• Collaborate closely with Algorithms and Machine Learning team members to push the boundaries of our current deep-learning models
• Get familiar with the Beacon data and software environment (Julia, Python, Pytorch)
• Work on literature reviews to spot the latest methods that may be useful to apply Deep learning models to large and heterogeneous Sleep datasets.
• Implement state-of-the-art methods on the Beacon datasets
• Design deep learning models to improve our algorithms on internal datasets
• Ensure your models' implementations are easy to use, well-documented, and well-tested by following best practices (Unit test, documentation, CI, non-regression, …).

• Last year student in an MSc/PhD in Computer Science, Machine learning, or a quantitative field Strong skills in Deep Learning, proven via successful experience(s)/project(s) Best practices in software engineering (test, Github, documentation, Dockerization, CI/CD) or willingness to learn and apply them Experience with Torch (preferred) or other deep-learning frameworks Rigor and desire to learn new topics Fluent in English

• What your internship will look like:
• Within one month, You will have performed a literature review and played with our internal libraries and models.
• Within three months: You will have implemented recent models from the literature and compared them with our internal models.
• Within six months: You will have proposed, implemented, and evaluated possible model improvements from the literature to fit our needs.

• Additional Information
• 🏡 Great office in the center of Paris (75009 - Grand Boulevard) 🍽 Swile (Tickets Restaurants) 🎽 Gymlib 🚈 50% pass Navigo or bike indemnities

The base salary range for this role is determined based on past experience, specific skills and qualifications. The base salary is one component of the total compensation package, which includes equity, PTO and other benefits.

At Beacon, we've found that cultural and scientific impact is driven most by those that lead by example. As such, we're always seeking new contributors whose work demonstrates an avid curiosity, a bias towards simplicity, an eye for composability, a self-service mindset, and - most of all - a deep empathy towards colleagues, stakeholders, users, and patients. We believe a diverse team builds more robust systems and achieves higher impact.