Beacon Biosignals

Precision Medicine

AlgorithmEngineer

$150–170k Remote Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Algorithm Engineer at Beacon Biosignals. Skills: Machine Learning, Deep Learning, Algorithm Development, Digital Signal Processing. Lead algorithm development lifecycle. Gather specifications and requirements”

What You'll Achieve.

Revolutionize precision medicine; Enable quantitative biomarker discovery; Power large-scale RWD/RWE studies; Change how patients are treated; Drive cultural and scientific impact; Build more robust systems; Achieve higher impact; Boost team efficiency; Encourage reusability; Enable rapid experimentation; Ensure algorithm implementations are user-friendly; Ensure algorithm implementations are well-documented; Ensure algorithm implementations are thoroughly tested; Shape the impact Beacon algorithms have

Industry & Context.

Precision Medicine
Problems you'll solve

Failure-analysis

What They're Looking For.

Must Have

4 years of industry experience in machine learning and deep learning, Experience with digital signal processing (DSP), Experience with statistics, Proficient in PyTorch or other deep learning frameworks, Familiar with latest Deep Learning advances, Follow and adopt best practices in software and ML engineering, Familiar with biosignals, medical imaging data, or large time-series datasets, Able to distill, discuss, and present complex technical topics

Nice to Have

PyTorch

What You'll Do.

Lead algorithm development lifecycle

Gather specifications and requirements

Curate and label data

Perform failure analysis

Select and implement methods

Introduce new model architectures

Improve best practices

Ensure algorithm implementations are user-friendly

Test algorithm implementations

Present results to stakeholders

Assist stakeholders in utilizing algorithms

Support client-facing projects

Shape algorithm impact for customers

How You'll Work.

Team & Collaboration

Thrive in a team environment; Recognize collaboration is essential; Recognize open communication is essential; Recognize continuous feedback is essential; Present results to key stakeholders; Assist stakeholders in utilizing algorithms; Support client-facing projects

Communication Scope

Distill complex technical topics; Discuss complex technical topics; Present complex technical topics

Full Job Description

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 Waveband 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 a Machine Learning engineer! 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. Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we also have in-person office hubs in Boston, New York City and Paris. What success looks like 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 experimentat

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