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Associate Machine Learning Jobs in Massachusetts

Our-Artificial Intelligence Machine Learning-(AI/ML) capabilities are critical accelerators to our ... As Associate Director, AI/ML Engineering, you will build state-of-the-art AI/ML tools and pipelines ...

Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical properties of macromolecules, developing ...

Coding and/or machine learning experience Key Responsibilities & Accountabilities * Conduct original research as the primary responsibility of the role. * Write and co-author research papers for ...

The Postdoctoral Research Associate will receive professional development training, gain valuable ... Effectively design, implement, and evaluate machine learning and computational methods * Work with ...

Industry/Sector Not Applicable Specialism Data Science Management Level Senior Associate & Summary ... machine learning algorithms and predictive modeling to solve complex business problems - Creating ...

Industry/Sector Not Applicable Specialism Oracle Management Level Senior Associate & Summary At PwC ... Responsibilities - Design and implement advanced AI and machine learning solutions - Analyze ...

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Associate Machine Learning information

See Massachusetts salary details

$26.5K

$139.3K

$341.5K

How much do associate machine learning jobs pay per year?

As of Jul 22, 2026, the average yearly pay for associate machine learning in Massachusetts is $139,272.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,900.00 and $193,100.00 per year, depending on experience, location, and employer.

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

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Engineer, and why are they important?

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by Associate Machine Learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What does an Associate Machine Learning Engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.
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 Associate Machine Learning jobs in Massachusetts? For Associate Machine Learning jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Associate Machine Learning jobs in Massachusetts look for? The top searched job categories for Associate Machine Learning jobs in Massachusetts are:
Postdoctoral Associate, Dorkenwald Lab

Postdoctoral Associate, Dorkenwald Lab

MIT Human Resources

Cambridge, MA • On-site

Other

Posted 19 days ago


Job description

POSTDOCTORAL ASSOCIATE, DORKENWALD LAB, McGovern Institute for Brain Research -  Dorkenwald laboratory, to develop computational reconstruction, analysis, and modeling approaches for connectomics datasets. The lab develops computational approaches to reconstruct, analyze, and model large-scale connectomes, aiming to uncover organizational principles of neuronal circuits and how circuit structure supports computation. Will lead research on one or more of the following areas: Automated proofreading & annotation at scale: Machine learning approaches for error detection, human-in-the-loop proofreading of automated cell reconstructions, active-learning approaches for efficient annotation, and self-supervision approaches for tokenizing image datasets and cell reconstructions; Circuit analysis & modeling: Analysis of cortical connectomes, including comparative analyses across ages/regions; hypothesis-driven tests of discovered circuit rules; pair analyses with data-constrained models (e.g., RNNs, dynamical systems) and simulations; Morphology representation & multi-modal linking: Learn representations of detailed cell morphologies to link across datasets (within connectomics) and across modalities (e.g., EM ↔ Patch-seq) to build multi-modal connectomic resources that provide the basis for analyses that combine, e.g., connectivity with transcriptomic information; and publish in leading venues, maintain high-quality, reproducible code, collaborate across McGovern/BCS and external collaborators, and mentor students.


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About MIT Human Resources

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Our mission is to advance a vibrant and diverse work community where individuals and groups thrive and contribute to MIT's excellence. We offer Support, Services, and Programs to enhance your work life. We're here to help. MIT is committed to helping employees achieve a healthy balance between their careers and the full lives they lead off-campus. Explore all of MIT's Work & Life resources, including MyLife Services, which provides 24/7 access to a network of experts available to help with life’s challenges.

Industry

Human resource programs administration

Company size

1 - 10 Employees

Headquarters location

Cambridge, MA, US

Year founded

2014