Algorithm Engineer, Beacon Biosignals
+ equity
AI brain monitoring
Open for applications
AI brain monitoring
101-200 employees
Open for applications
+ equity
101-200 employees
Company mission
To improve human health by enabling rapid, targeted interventions through advanced brain analytics.
Role
Who you are
- You have more than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields, with a proven track record of bringing algorithms into production
- You are experienced with digital signal processing (DSP) and statistics and care about using the right tool for the job, which in many cases might not be machine learning or deep learning
- You are proficient in using PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models
- You are familiar with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
- You follow and adopt best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking
- You are familiar with biosignals, medical imaging data, or large time-series datasets, or are enthusiastic about learning more in the domain
- You thrive in a team environment, recognizing that collaboration, open communication, and continuous feedback are essential for collective success
- You are able to distill, discuss, and present complex technical topics in a way that is appropriate for the audience at hand, both internally and externally
- You are excited to participate in the entire algorithm development lifecycle, which spans scoping, data wrangling, algorithm development/experimentation, formal validation, quality/regulatory documentation, production deployment, and working with clients who might benefit from these algorithms
What the job involves
- Participate in and lead the entire biosignal-based algorithm development lifecycle for medical devices including specifications and requirements gathering, data curation and labeling, development, failure-analysis, production, maintenance, and documentation
- Select, implement, and develop the most appropriate method for each problem, knowing when to apply deep learning techniques and when other methods are more effective
- Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce new model architectures and algorithmic techniques, and refine the codebase to encourage reusability where needed to enable rapid experimentation
- Spread and improve our best practices to ensure algorithm implementations are user-friendly, well-documented, and thoroughly tested, including unit tests, comprehensive documentation, CI, and non-regression testing
- Present results to key stakeholders and assist them in utilizing algorithms for client engagement
- Support the client-facing projects to understand and shape the impact Beacon algorithms have for our customers, both for existing deployed algorithms, and future algorithm development
Our take
The brain has long been considered one of the most mysterious fields of medicine - and psychiatric and neurological diseases therefore one of the least understood. Better access to real-time brain data, however, could change this and that’s exactly what Beacon Biosignals is hoping to deliver.
Beacon Biosignals has developed an AI-powered EEG platform designed to better measure and understand brain activity. Its technology includes a lightweight wearable headband that tracks brain signals during sleep and at home, feeding huge amounts of real-world data into machine-learning models. The aim is to help researchers analyze brain activity faster, identify new biomarkers, and improve the development of treatments for neurological and psychiatric disorders.
The company recently raised major funding and acquired home sleep testing specialist CleveMed as it expands its AI-powered brain health platform. Its FDA-cleared wearable headband is already being used in more than 40 clinical trails studying conditions like depression, Alzheimer's, Parkinson's, and sleep disorders. By collecting millions of hours of brain activity data, Beacon hopes to help researchers detect diseases earlier and better understand how the brain changes over time.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
40% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Apr 2026
$11m
SERIES B
Nov 2025
$86m
SERIES B
Total funding: $127.5m
Company HQ
Beacon Hill, Boston, MA
Leadership
Jarrett Revels
(Co-Founder & CTO)Prior to co-founding Beacon Biosignals in 2019, Jarrett spent 4 years at Massachusetts Institute of Technology, joining as a Scientific Programmer before become a Performance Engineer then Research Software Developer.
Jacob Donoghue
(Co-Founder & CEO)Jacob completed a Neuroscience PhD at Massachusetts Institute of Technology, and received his MD from Harvard Medical School. Subsequently they carried out research for Cyberkinetics Neurotechnology Systems and Massachusetts General Hospital.
Brandon Westover
(Co-Founder)Received their MD from Washington University School of Medicine, then took up a Neurology residency at Harvard Medical School. Alongside their work at Beacon Biosignals, Brandon is also an HST Faculty member at MIT and a Neurologist/Medical AI Researcher at Massachusetts General Hospital.
Syd Cash
(Co-Founder)Syd is an Associate Professor at Harvard Medical School, who also leads the Center for Neurotechnology and Neurorecovery at MGH.
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