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Remote Machine Learning Architect Jobs in Massachusetts

Beacon Biosignals

Senior Algorithm Engineer

Boston, MA · On-site +1

$170K - $190K/yr

As part of Beacon's analytics and machine learning domain, you'll work alongside fellow data ... This is a fully remote role based anywhere in the U.S. What success looks like * Participate in and ...

Adidev Technologies Inc

ML/AI Engineers

Boston, MA · Remote

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... Key Responsibilities: · Architect and refine sophisticated ML models and algorithms, translating ...

C the Signs

AI Data Engineer

Boston, MA · On-site +1

$124K - $149K/yr

Collaborate with data scientists and machine learning engineers to understand data requirements for ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

Boston, MA · On-site +1

$124K - $149K/yr

Collaborate with data scientists and machine learning engineers to understand data requirements for ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Beacon Biosignals

Algorithm Engineer

Boston, MA · On-site +1

$150K - $170K/yr

As part of Beacon's analytics and machine learning domain, you'll work alongside fellow data ... Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we ...

Beacon Biosignals

Algorithm Engineer

Boston, MA · On-site +1

$150K - $170K/yr

As part of Beacon's analytics and machine learning domain, you'll work alongside fellow data ... Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we ...

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All JobsMachine Learning Architect JobsRemote Machine Learning Architect JobsRemote Machine Learning Architect Jobs in Massachusetts

Remote Machine Learning Architect information

What is a remote machine learning architect?

A Remote Machine Learning Architect is a professional who designs, builds, and oversees machine learning systems and infrastructure while working remotely. They collaborate with data scientists, engineers, and stakeholders to define system architecture, select appropriate algorithms, and ensure scalable deployment of machine learning models. Their responsibilities include setting technical standards, optimizing workflows, and ensuring integration with existing IT infrastructure, all accomplished through remote communication and collaboration tools. This role requires strong expertise in machine learning, cloud platforms, and software engineering.

How does a remote machine learning architect typically collaborate with distributed teams to deliver successful projects?

As a Remote Machine Learning Architect, effective collaboration with globally distributed teams is essential. You will often coordinate with data scientists, software engineers, and business stakeholders via virtual meetings, shared documentation, and project management tools. Regular communication, clear documentation of model designs, and version control practices are crucial to ensure alignment and smooth integration of machine learning solutions. Adopting agile methodologies and being proactive in addressing time zone differences help maintain project momentum and foster a productive team environment.

What are the key skills and qualifications needed to thrive as a remote machine learning architect, and why are they important?

To thrive as a Remote Machine Learning Architect, you need deep expertise in machine learning algorithms, model development, and a solid background in computer science or related fields, often supported by an advanced degree. Familiarity with cloud platforms (such as AWS, Azure, or GCP), deep learning frameworks (like TensorFlow or PyTorch), and relevant certifications are typically expected. Strong problem-solving, communication, and project management skills help you collaborate effectively with distributed teams and stakeholders. These skills and qualities are crucial for designing scalable ML solutions that drive business value in a remote work environment.

What is the difference between Remote Machine Learning Architect vs Data Scientist?

AspectRemote Machine Learning ArchitectData Scientist
Required CredentialsMaster's or PhD in CS, AI, or related fields; certifications in ML frameworksMaster's in Data Science, Statistics, or related; certifications in data analysis tools
Work EnvironmentDesigning ML systems, collaborating with engineering teams, remote or on-siteAnalyzing data, building models, often remote or in-office
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

Remote Machine Learning Architects focus on designing and implementing scalable ML systems, while Data Scientists analyze data and build models. Both roles require advanced degrees and often overlap in skills, but their core responsibilities differ in scope and focus.

What are popular job titles related to Remote Machine Learning Architect jobs in Massachusetts?

For Remote Machine Learning Architect jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Remote Machine Learning Architect jobs in Massachusetts look for?

The top searched job categories for Remote Machine Learning Architect jobs in Massachusetts are:

What cities in Massachusetts are hiring for Remote Machine Learning Architect jobs?

Cities in Massachusetts with the most Remote Machine Learning Architect job openings:

Remote Machine Learning Architect jobs near you
Infographic showing various Remote Machine Learning Architect job openings in Massachusetts as of July 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior Algorithm Engineer

Beacon Biosignals

Boston, MA • On-site, Remote

$170K - $190K/yr

Full-time

PTO

Re-posted 9 days ago

Job description

Beacon Biosignals is seeking a Senior Algorithm and 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 work practices ensure a first-class remote work experience, but we also have in-person office hubs in Boston, New York City and Paris. This is a fully remote role based anywhere in the U.S.

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 5 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 proficient with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
  • You follow and spread best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.
  • You are experienced 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 $170,000 - $190,000. 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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