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Junior Machine Learning Jobs in Massachusetts (NOW HIRING)

Manulife

Senior Machine Learning Engineer

Boston, MA · Hybrid

$107K - $199K/yr

Mentor Data Scientists and Junior Engineers in best practices for programming, Machine Learning Operations (MLOps), and Large Language Model Operations (LLMOps); and in use of various big data ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Provide technical leadership to junior scientists, guiding the transition of R&D concepts into ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

DraftKings, Inc.

Senior Machine Learning Engineer

Boston, MA · On-site

$133K - $175K/yr

The Crown Is Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale ... Mentor junior engineers and contribute to shared tooling, documentation, and process improvements ...

DraftKings

Senior Machine Learning Engineer

Boston, MA · On-site

$133K - $175K/yr

The Crown Is Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale ... Mentor junior engineers and contribute to shared tooling, documentation, and process improvements ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Provide technical leadership to junior scientists, guiding the transition of R&D concepts into ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

Whoop

Senior Manager, Machine Learning

Boston, MA · On-site

Our Health Machine Learning team develops the algorithms and models that power health features used ... Develop the next layer of leadership, coach junior and seniorICs, manage performance with clarity ...

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Junior Machine Learning information

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$8

$29

$51

How much do junior machine learning jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for junior machine learning in Massachusetts is $29.44, according to ZipRecruiter salary data. Most workers in this role earn between $17.84 and $36.25 per hour, depending on experience, location, and employer.

What is the difference between Junior Machine Learning vs Data Scientist?

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What does a junior machine learning engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

What types of projects and tasks can a junior machine learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer?

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.
What are the most commonly searched types of Machine Learning jobs in Massachusetts? The most popular types of Machine Learning jobs in Massachusetts are:
What are popular job titles related to Junior Machine Learning jobs in Massachusetts? For Junior Machine Learning jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning jobs in Massachusetts look for? The top searched job categories for Junior Machine Learning jobs in Massachusetts are:
What cities in Massachusetts are hiring for Junior Machine Learning jobs? Cities in Massachusetts with the most Junior Machine Learning job openings:
Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $61,234 per year, or $29.4 per hour.

Machine Learning Ops Engineer II

Boston Children's Hospital

Boston, MA • On-site

$120 - $160/hr

Other

Posted yesterday

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Job description

Position Summary

The ML Ops Engineer II at Boston Children’s Hospital is an integral part of the Cardio Engineering Team within the Department of Cardiac Surgery (https://www.cardioengineering.org). This role is pivotal in developing and scaling advanced AI and machine learning projects, enhancing data frameworks, and optimizing data flows to support the hospital’s strategic initiatives. The ML Ops Engineer II works in close collaboration with data scientists and various stakeholders across the hospital to develop solutions that improve patient care outcomes and operational efficiency. Focused on innovation and technological advancement, the ML Ops Engineer II ensures the robust integration of data science into clinical and administrative processes.

Key Responsibilities
  • Leads the development, implementation, and scaling of machine learning models and other AI-driven projects within the Data & Analytics team.
  • Collaborates with data scientists and other stakeholders to translate complex model requirements into operational systems.
  • Ensures robust and scalable infrastructure to support AI deployments and continuous learning cycles.
  • Utilizes SQL, Python, and dbt to build and optimize data models that support complex queries and analyses essential for AI projects.
  • Interprets data analysis results and communicates findings to both technical and non-technical stakeholders, ensuring actionable insights.
  • Works closely with healthcare professionals, researchers, and administrators to define and refine data requirements for AI applications.
  • Provides expert guidance and mentorship to junior engineers and other team members on data science and engineering best practices.
  • Manages multiple AI and data science projects, ensuring effective communication, adherence to timelines, and delivery within budget.
  • Acts as a project lead, coordinating efforts across different teams and ensuring project milestones are met.
  • Develops project plans, tracks progress, and adjusts resources and timelines as needed to ensure successful project delivery.
  • Partner with Innovation and Digital Health Accelerator to operationalize AI within BCH.
  • Actively seeks and integrates new technologies and methodologies to enhance the capabilities of AI projects and data processes.
  • Stays informed of the latest industry trends and technologies, advocating for the adoption of innovative solutions that can provide competitive advantages.
  • Encourages and leads initiatives to explore and adopt innovative solutions that provide competitive advantages.
  • Ensures the integrity and reliability of data used in AI and machine learning projects, adhering to quality standards and compliance requirements.
  • Maintains comprehensive documentation of all data processes, models, and code to ensure reproducibility and adherence to internal and external regulations.
  • Conducts regular code reviews and quality checks to ensure best practices are followed and standards are met.
Minimum Qualifications Education:
  • A bachelor's degree in computer science, engineering or a related field; master's degree is preferred.
Experience:
  • 3-5 years of relevant experience in data engineering, including project leadership responsibilities and advanced technical contributions to AI or machine learning projects.
  • Proficient in SQL and an understanding of database management systems.
  • Familiarity with ETL tools; experience with debt is highly advantageous.
  • Familiarity with nnU-Net and other CNN and deep learning approaches for image segmentation and processing
  • Strong capabilities in Python or another advanced scripting language, essential for AI and machine learning model development.
  • Experience with cloud-based data platforms and tools such as AWS, Azure, or GCP is a plus.
  • Demonstrated experience in managing multiple projects, including developing project plans, tracking progress, and adjusting resources and timelines.
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