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Remote Biomedical Signal Processing Engineer Jobs in Boston, MA

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Remote Biomedical Signal Processing Engineer information

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$58.1K

$142.7K

$210.2K

How much do remote biomedical signal processing engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for remote biomedical signal processing engineer in Boston, MA is $142,697.00, according to ZipRecruiter salary data. Most workers in this role earn between $117,900.00 and $160,200.00 per year, depending on experience, location, and employer.

What does a remote biomedical signal processing engineer do?

A Remote Biomedical Signal Processing Engineer analyzes and interprets physiological signals—such as ECG, EEG, or EMG—using advanced computational and mathematical methods. They work remotely to develop algorithms and software that aid in medical diagnostics, patient monitoring, and healthcare research. Their role often involves cleaning, filtering, and extracting meaningful information from complex biological data to support clinical decisions or scientific studies. Collaboration with medical professionals and teams is common, and strong knowledge of signal processing, biomedical engineering, and programming is essential.

What are the key skills and qualifications needed to thrive as a remote biomedical signal processing engineer?

To thrive as a Remote Biomedical Signal Processing Engineer, you need expertise in signal processing, biomedical engineering, and a strong background in mathematics and statistics, usually supported by a relevant degree. Familiarity with tools like MATLAB, Python (NumPy, SciPy), and experience with medical device data protocols and regulatory standards are commonly required. Strong problem-solving, self-motivation, and clear communication skills help you work effectively in a remote, interdisciplinary environment. These abilities are crucial for developing accurate, regulatory-compliant solutions that improve healthcare outcomes while collaborating remotely with diverse teams.

What are some typical challenges faced by remote biomedical signal processing engineers, and how can they be addressed?

Remote Biomedical Signal Processing Engineers often face challenges related to collaborating with interdisciplinary teams, ensuring data security, and accessing necessary hardware for testing algorithms. To overcome these, it's important to establish clear communication channels with colleagues, make use of secure data transfer protocols, and leverage remote access to lab equipment or simulators when possible. Regular virtual meetings and documentation can help maintain alignment with project goals and facilitate effective teamwork.

What is the difference between Remote Biomedical Signal Processing Engineer vs Remote Medical Data Analyst?

AspectRemote Biomedical Signal Processing EngineerRemote Medical Data Analyst
Required CredentialsBachelor's or Master's in Biomedical Engineering, Electrical Engineering, or related fields; knowledge of signal processingBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis tools
Work EnvironmentResearch labs, healthcare tech companies, hospitals; focus on signal dataHealthcare organizations, research institutions; focus on large datasets
Employer & Industry UsageMedical device companies, biotech firms, hospitalsHealthcare providers, research organizations, health tech startups

While both roles involve working with healthcare data, Remote Biomedical Signal Processing Engineers focus on analyzing and developing algorithms for biomedical signals like ECG or EEG. Remote Medical Data Analysts interpret large health datasets to derive insights. The roles differ mainly in technical focus and data types but often collaborate within healthcare tech environments.

What are the most commonly searched types of Biomedical Signal Processing Engineer jobs in Boston, MA?

The most popular types of Biomedical Signal Processing Engineer jobs in Boston, MA are:

What are popular job titles related to Remote Biomedical Signal Processing Engineer jobs in Boston, MA?

For Remote Biomedical Signal Processing Engineer jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Remote Biomedical Signal Processing Engineer jobs in Boston, MA look for?

The top searched job categories for Remote Biomedical Signal Processing Engineer jobs in Boston, MA are:

What cities near Boston, MA are hiring for Remote Biomedical Signal Processing Engineer jobs?

Cities near Boston, MA with the most Remote Biomedical Signal Processing Engineer job openings:

Remote Biomedical Signal Processing Engineer jobs near you
Infographic showing various Remote Biomedical Signal Processing Engineer job openings in Boston, MA as of August 2026, with employment types broken down into 80% Full Time, 15% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $142,697 per year, or $68.6 per hour.

Algorithm Engineer

Beacon Biosignals

Boston, MA • On-site, Remote

$150K - $170K/yr

Full-time

PTO

Re-posted 4 days ago

Job description

Beacon Biosignals is transforming precision medicine for the brain, from clinical development to clinical care. For Life Sciences partners, we offer the leading at-home EEG platform for clinical development of novel therapeutics for neurological, psychiatric, and sleep disorders. Our Diagnostics business is building the most comprehensive at-home platform for precision diagnostics, combining EEG and cardiopulmonary signals to deliver reimbursable assessments for sleep and central nervous system disorders. Together, we're changing the way patients are diagnosed and treated for any disorder that affects brain physiology.
Beacon Biosignals is seeking a Machine Learning engineer!
As part of Beacon's analytics and machine learning domain, you'll work alongside fellow data scientists, neuroscientists, engineers, and clinicians to scope, build, deploy, and maintain the machine and deep learning models that analyze brain and biosignal data for advancing sleep, neurological, and psychiatric therapy development.
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 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.

What you will bring
  • 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.

The US-based salary range for this role is $150,000 - $170,000. Salary ranges are determined using current market compensation data for this role and adjusted based on experience, skills, and location. 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.
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