Algorithm Engineer

Beacon Biosignals

. 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 .

Posted 9/15/2026full-timeRemote • United StatesMid-LevelSenior💰 $150,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning and deep learning for medical devices, with a strong focus on algorithm development, production deployment, and collaboration with interdisciplinary teams. Proficient in implementing best practices for software and ML engineering, including testing and documentation.

Highest-signal resume keywords

Machine LearningDeep LearningDigital Signal ProcessingPyTorchAlgorithm Development Lifecycle

ATS Keywords

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Hard Skills

Machine LearningDeep LearningDigital Signal ProcessingStatisticsAlgorithm ImplementationModel Architecture DevelopmentData CurationTesting Best PracticesVersion ControlExperiment Tracking

Soft Skills

CollaborationOpen CommunicationPresentation SkillsContinuous Feedback

Tools & Technologies

PyTorchDockerCI/CDUnit TestingCode Reviews

Industry Keywords

Health SciencesBiosignalsMedical ImagingRegulated FieldsLarge Time-Series Datasets

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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 appropriate methods for each problem, including determining when to use deep learning or other methods
Enhance internal deep learning and machine learning tools to improve team efficiency
Introduce new model architectures and algorithmic techniques
Refine the codebase to encourage reusability and enable rapid experimentation
Improve best practices for user-friendly, well-documented, thoroughly tested algorithm implementations, including unit tests, CI, and non-regression testing
Present results to key stakeholders and assist with algorithm use for client engagement
Support client-facing projects and help shape the impact of algorithms for customers
Collaborate with data scientists, neuroscientists, engineers, and clinicians to scope, build, deploy, and maintain machine and deep learning models analyzing brain and biosignal data

Requirements

What you’ll need

More than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields
Proven track record of bringing algorithms into production
Experience with digital signal processing (DSP) and statistics
Proficiency using PyTorch or other deep learning frameworks
Familiarity with recent deep learning advances, including Transformer/ViT, large-scale modeling, and large model training
Knowledge of software and ML engineering best practices, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking
Familiarity with biosignals, medical imaging data, or large time-series datasets, or enthusiasm for learning more in the domain
Ability to distill, discuss, and present complex technical topics appropriately for internal and external audiences
Excitement to participate in the entire algorithm development lifecycle, including scoping, data wrangling, experimentation, formal validation, quality/regulatory documentation, production deployment, and client collaboration
Collaboration, open communication, and continuous feedback are essential for collective success

Benefits

Comp & perks

Equity
PTO
Asynchronous remote work practices
In-person office hubs in Boston, New York City, and Paris