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Associate Machine Learning Jobs (NOW HIRING)

Empower

Senior Machine Learning Solutions Architect

... our associates dedicate thousands of hours to volunteering for causes that matter most to them ... As a Senior Machine Learning Solution Architect, you will shape how machine learning and advanced ...

Manulife Financial

$175K/yr

Mentor associates and peers on MLOps best practices. Work Arrangement requirement: Hybrid from ... machine learning experience. Experience must include the following, which may be gained ...

Walmart

Staff, Machine Learning Engineer

Pea Ridge, AR · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI driven intelligent products for the Associate Productivity and Experience team, with the ability to ...

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Associate Machine Learning information

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How much do associate machine learning jobs pay per year?

As of Sep 11, 2026, the average yearly pay for associate machine learning in the United States is $133,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $202,000.00 per year, depending on experience, location, and employer.

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 key skills and qualifications needed to thrive as an associate machine learning engineer?

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 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 cities are hiring for Associate Machine Learning jobs?

Cities with the most Associate Machine Learning job openings:

What are the most commonly searched types of Machine Learning jobs?

The most popular types of Machine Learning jobs are:

What states have the most Associate Machine Learning jobs?

States with the most job openings for Associate Machine Learning jobs include:

What are popular job titles related to Associate Machine Learning jobs?

For Associate Machine Learning jobs, the most frequently searched job titles are:

Associate Machine Learning jobs near you

Principal Machine Learning Engineer

Philadelphia, PA

Delan Associates, Inc
Engineering Professional Services • 51 - 200 employees
Learn more about Delan Associates, Incexternal

Contractor

Re-posted 28 days ago

Job description

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.

Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.

Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.

Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.

Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.

Move quickly from data exploration to prototype to validated model to production-ready capability.

Required Qualifications

Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.

5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.

3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.

Strong hands-on experience with Python and SQL.

Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.

Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.

Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.

Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.

Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.

Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership

Employment Type: CONTRACTOR

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