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Machine Learning Engineer Opt Jobs in Cambridge, MA

Senior Machine Learning Engineer

Boston, MA ยท On-site +1

$161K - $246K/yr

Overview: The ASUS Robotics & AI Center is seeking a Senior Machine Learning Engineer to join our global research and development team. This role centers on leading the design and delivery of ...

Senior Machine Learning Engineer

Boston, MA ยท Remote

$140K - $190K/yr

By joining our team as a Senior Machine Learning Engineer , you will play a pivotal role in building cutting-edge AI products that directly impact how new therapies reach patients. We're looking for ...

Machine Learning Engineer

Boston, MA ยท On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

About the role You'll be the founding ML engineer who owns our matching algorithms from exploration ... Real ranking and matching modeling fluency - learning-to-rank, retrieval and re-rank patterns, not ...

Showing results 41-60

Machine Learning Engineer Opt information

See Cambridge, MA salary details

$34.4K

$140.7K

$211.5K

How much do machine learning engineer opt jobs pay per year?

As of Sep 3, 2026, the average yearly pay for machine learning engineer opt in Cambridge, MA is $140,741.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,900.00 and $169,400.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

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

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are popular job titles related to Machine Learning Engineer Opt jobs in Cambridge, MA?

For Machine Learning Engineer Opt jobs in Cambridge, MA, the most frequently searched job titles are:

What cities near Cambridge, MA are hiring for Machine Learning Engineer Opt jobs?

Cities near Cambridge, MA with the most Machine Learning Engineer Opt job openings:

Infographic showing various Machine Learning Engineer Opt job openings in Cambridge, MA as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 27% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $140,741 per year, or $67.7 per hour.

Senior Machine Learning Engineer Ai/Machine Learning/Boston

Motion Recruitment Partners, LLC

Boston, MA โ€ข On-site

$133K - $175K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted yesterday


Job description


A full-service product development consultancy specializing in medical devices, robotic systems, and automation is hiring a Senior Machine Learning Engineer. In this position, you'll be responsible for owning the entire ML lifecycle, transforming raw sensor data into reliable models that run on constrained hardware. You will collaborate closely with embedded, hardware, and software teams to design and implement data pipelines, as well as optimize and deploy models for signal processing and anomaly detection on real-world devices.
As a core team member, you'll also help build out MLOps infrastructure, participate in sensor selection and integration, and ensure seamless validation and delivery of AI features within device software. You'll document model development to support both internal quality processes and regulatory submissions, ensuring your solutions are robust, maintainable, and production ready.
Required Skills & Experience
  • Strong proficiency in Python
  • Hands on experience in PyTorch or TensorFlow
  • Experience deploying models to edge using TFLite, ONNX, CoreML, TensorRT, or equivalent
  • Experience building sensor data pipelines
  • Proficiency with MLOps
  • Solid Software engineering fundamentals
  • Proficiency in C or C++
Desired Skills & Experience
  • 5 years of machine learning engineering or applied ML
  • Experience with physiological signal processing for medical or wearable applications
  • Background in robotics, or autonomous systems
  • Experience in a startup or small team
  • Degree in a relevant field
What You Will Be Doing
Daily Responsibilities
  • 100% Hands On
  • Develop and troubleshoot workflows for collecting, cleaning, and organizing sensor data.
  • Build and refine ML models for real-time device applications and performance improvements
  • Work closely with firmware teams to embed and test AI features on hardware platforms
  • Set up and oversee tools for tracking experiments, automating evaluations and managing deployments
  • Analyze model behavior, ensure reliability and resolve issues to maintain high-quality outputs
The Offer
  • Bonus OR Commission eligible
You will receive the following benefits
  • Medical Insurance
  • Dental Benefits
  • Vision Benefits
  • Paid Time Off (PTO)
  • 401(k) {including match - if applicable}
Applicants must be currently authorized to work in the US on a full-time basis now and in the future.