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Machine Learning Data Associate Jobs in Philadelphia, PA

Vanguard

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

This role partners closely with quantitative researchers, data scientists, and investment teams to engineer, deploy, and operate production-grade machine learning models that drive research ...

New

asrcfh

Senior Machine Learning Engineer

Moorestown, NJ

$103K - $141K/yr

Develops hypotheses based on data analysis and trends, and compares real-time results with ... Basic knowledge of machine learning techniques including popular frameworks (e.g., PyTorch or ...

asrcfh

Senior Machine Learning Engineer

Moorestown, NJ · On-site

$103K - $141K/yr

Develops hypotheses based on data analysis and trends, and compares real-time results with ... Basic knowledge of machine learning techniques including popular frameworks (e.g., PyTorch or ...

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Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for machine learning data associate in Philadelphia, PA is $18.91, according to ZipRecruiter salary data. Most workers in this role earn between $15.53 and $20.14 per hour, depending on experience, location, and employer.

What is a machine learning data associate?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data associate?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

How does a machine learning data associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

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

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

How do I become a machine learning data associate?

To become a machine learning data associate, candidates typically need a high school diploma or equivalent, with some roles preferring a bachelor's degree in computer science, data science, or related fields. Relevant skills include data annotation, understanding of machine learning concepts, and proficiency with tools like Excel, SQL, or data labeling platforms. Gaining experience through internships or certifications can improve job prospects in this field.

Is a Machine Learning Data Associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Compensation and job satisfaction vary depending on the employer and location, but it generally provides a solid foundation for a career in machine learning or data analysis.

What cities near Philadelphia, PA are hiring for Machine Learning Data Associate jobs?

Cities near Philadelphia, PA with the most Machine Learning Data Associate job openings:

Machine Learning Data Associate jobs near you
Infographic showing various Machine Learning Data Associate job openings in Philadelphia, PA as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $39,328 per year, or $18.9 per hour.

Senior Machine Learning Engineer

Malvern, PA • On-site

Vanguard
Photography Services • 1 - 5K employees
Learn more about Vanguardexternal

$102K - $140K/yr

Full-time

Posted 3 days ago

New

Job description

Join a dynamic team supporting model development and operations for research and insights across Investment Management. This role partners closely with quantitative researchers, data scientists, and investment teams to engineer, deploy, and operate production-grade machine learning models that drive research, analytics, and business insights. You will be responsible for building and maintaining the end-to-end ML lifecycle, including model pipelines, feature engineering workflows, automated training and deployment processes, model monitoring, and production operations. The ideal candidate combines strong software engineering fundamentals with hands-on experience implementing MLOps best practices and operating machine learning solutions on AWS SageMaker. Expertise in Python, cloud-native architectures, and scalable data processing is essential.

Core Responsibilities 

  • Design, build, and maintain end-to-end machine learning pipelines from research through production deployment. 
  • Engineer scalable training, inference, and retraining workflows using AWS SageMaker. 
  • Develop and maintain feature engineering, feature storage, and data preparation pipelines. 
  • Automate model deployment, testing, validation, and release processes using CI/CD practices. 
  • Build batch, real-time, and event-driven architectures. 
  • Implement model monitoring for performance, drift detection, data quality, and operational health. 
  • Partner with quantitative researchers and data scientists to productionalize research models. 
  • Manage model versioning, lineage tracking, experiment management, and reproducibility. 
  • Optimize model performance, scalability, reliability, and cloud cost efficiency. 
  • Establish engineering standards, testing frameworks, and governance controls for ML solutions. 
  • Support production operations, incident response, and continuous improvement of deployed models. 

Required Qualifications:

  • Minimum of eight years related work experience, with at least three years of development experience.
  • Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
  • Experience in software engineering, machine learning engineering, data engineering, or a related technical discipline. 
  • Strong experience building and deploying machine learning solutions in production environments. 
  • Expertise in Python and modern data science libraries (Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, or similar). 
  • Hands-on experience with AWS services, including SageMaker 
  • Experience building and maintaining machine learning pipelines, feature engineering workflows, and model deployment processes. 
  • Knowledge of MLOps practices, including CI/CD, model versioning, experiment tracking, monitoring, and automated retraining. 
  • Strong understanding of software development lifecycle practices, testing strategies, and production support. 
  • Ability to work effectively with researchers, data scientists, and business stakeholders to deliver business outcomes. 

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Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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