ZipRecruiter

Log In

1

Learning Engineer Jobs in Philadelphia, PA (NOW HIRING)

AKUVO LLC

Senior Data & Machine Learning Engineer

Malvern, PA · On-site

$112K - $134K/yr

THE OPPORTUNITY AKUVO is seeking a hands-on Senior Data & Machine Learning Engineer to build and own the production lifecycle of our proprietary predictive models and scores. This is a depth role ...

Vanguard Group

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

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.

Vangard, Inc.

Senior Machine Learning Engineer

Malvern, PA

$102K - $140K/yr

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.

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Wilmington, DE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

next page

Showing results 1-20

All JobsLearning Engineer JobsLearning Engineer Jobs in Philadelphia, PA

Learning Engineer information

See Philadelphia, PA salary details

$38.3K

$116.9K

$193.2K

How much do learning engineer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for learning engineer in Philadelphia, PA is $116,917.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,800.00 and $152,900.00 per year, depending on experience, location, and employer.

What is a 900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as AI research directors, machine learning executives, or senior data scientists, often requiring advanced skills, extensive experience, and sometimes equity or performance-based compensation. These positions are usually found in leading tech companies or startups with significant AI investments and may involve managing teams, developing innovative algorithms, or overseeing AI strategy. Compensation at this level reflects the value of expertise in AI development, deployment, and strategic planning.

What does a learning engineer do?

A learning engineer designs, develops, and implements educational programs and digital learning solutions. They analyze learning needs, create instructional content, and often use tools like learning management systems (LMS) to enhance training effectiveness.

What is the difference between Learning Engineer vs Instructional Designer?

AspectLearning EngineerInstructional Designer
Required CredentialsBachelor's or master's in education, instructional design, or related fields; familiarity with e-learning toolsBachelor's or master's in education, instructional design, or related fields; expertise in curriculum development
Work EnvironmentCollaborates with developers, data analysts, and educators to build digital learning solutionsDesigns and develops educational content and curricula for various learning settings
Employer & Industry UsageTech companies, online education platforms, corporate trainingSchools, universities, corporate training departments

Learning Engineers focus on developing and implementing innovative digital learning solutions using technology and data analysis, while Instructional Designers primarily create educational content and curricula. Both roles require similar educational backgrounds and often work in overlapping industries, but their core responsibilities differ in approach and focus.

What is a Learning Engineer?

A Learning Engineer is a professional who designs, develops, and implements educational experiences using principles from learning science, technology, and instructional design. They work to create effective learning environments, often integrating digital tools and data analytics to enhance teaching and learning outcomes. Learning Engineers collaborate with educators, subject matter experts, and technologists to build solutions that address specific educational challenges.

Will MLE be replaced by AI?

As a Learning Engineer, AI is a tool that can enhance machine learning workflows, but it is unlikely to fully replace the need for human expertise in designing, implementing, and maintaining machine learning systems. MLE roles require skills in data handling, model evaluation, and system deployment that go beyond automation. AI can automate certain tasks, but human oversight remains essential for ensuring ethical, effective, and reliable machine learning solutions.

How do Learning Engineers typically collaborate with subject matter experts and instructional designers during course development?

Learning Engineers play a pivotal role in bridging technical solutions and educational goals. They often work closely with subject matter experts to deeply understand the content, ensuring its accurate representation in digital formats. Collaboration with instructional designers is essential, as Learning Engineers translate pedagogical strategies into interactive and accessible learning experiences, utilizing technologies such as learning management systems, analytics, and multimedia tools. Effective communication and iterative feedback are key, as these teams work together to design, test, and refine educational products that maximize learner engagement and success.

What engineer makes $500,000 a year?

Senior software engineers, especially those in high-demand fields like machine learning, AI, or working at major tech companies, can earn $500,000 or more annually through base salary, bonuses, and stock options. Achieving this level typically requires extensive experience, advanced skills, and often working in competitive markets or leadership roles.

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

To thrive as a Learning Engineer, you need expertise in instructional design, learning science, and educational technology, often supported by a degree in education, instructional design, or a related field. Familiarity with learning management systems (LMS), authoring tools like Articulate or Adobe Captivate, and data analytics platforms is typically required. Strong collaboration, problem-solving, and communication skills distinguish top performers in this role. These competencies are crucial for designing effective, scalable learning experiences that meet diverse learner needs and organizational goals.
What are popular job titles related to Learning Engineer jobs in Philadelphia, PA? For Learning Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Learning Engineer jobs in Philadelphia, PA look for? The top searched job categories for Learning Engineer jobs in Philadelphia, PA are:
Learning Engineer jobs near you
Infographic showing various Learning Engineer job openings in Philadelphia, PA as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $116,917 per year, or $56.2 per hour.
Principal Machine Learning Engineer

Principal Machine Learning Engineer

Apetan Consulting llc

Philadelphia, PA • On-site

$80 - $150/hr

Contractor

Posted 10 days ago

Job description

Title: Principal Machine Learning Engineer

Duration: 6 Mos C2H (without sponsorship)

Location: Hybrid in Philadelphia, PA onsite Tue & Wed each week (Local candidates preferred but, those willing to relocate are acceptable)

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