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Temporary Meta Machine Learning Jobs in Camden, NJ

Meta Resources Group

AI Program Manager

Philadelphia, PA · Hybrid

$85 - $90/hr

Lead the end-to-end delivery of multiple AI, Machine Learning, and Generative AI programs across global business functions. * Drive AI initiatives from business intake through planning, delivery ...

Villanova University

Mentor

Villanova, PA · On-site

$27.58 - $29.12/hr

Temp/Intern Location: Villanova, PA Work Schedule: temporary Approximate Number of Hours per Week 5 ... Demonstrated experience with Artificial Intelligence and/or Machine Learning (academic, research ...

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How much do temporary meta machine learning jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for temporary meta machine learning in Camden, NJ is $23.02, according to ZipRecruiter salary data. Most workers in this role earn between $19.90 and $25.72 per hour, depending on experience, location, and employer.

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

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

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What cities near Camden, NJ are hiring for Temporary Meta Machine Learning jobs?

Cities near Camden, NJ with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you
Infographic showing various Temporary Meta Machine Learning job openings in Camden, NJ as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $47,890 per year, or $23 per hour.

AI Program Manager

Meta Resources Group

Philadelphia, PA • Hybrid

$85 - $90/hr

Full-time

Posted 29 days ago

Job description

This is a remote position.

Our client, a leading global Healthcare and Consulting organization, is seeking an experienced AI Delivery Program Manager to lead enterprise-scale Artificial Intelligence initiatives across global business functions. This is not a traditional Program Management role. The ideal candidate must have recent (within the last two years) hands-on experience delivering AI, Machine Learning, or Generative AI programs, with proven expertise managing AI work products through Agile delivery methodologies, AI Factory operating models, and Value Team delivery frameworks. The successful candidate will partner with business leaders, AI Engineering, Data Science, MLOps, Product, and Enterprise Architecture teams to successfully deliver AI initiatives from ideation through production deployment while ensuring measurable business value and governance throughout the delivery lifecycle.

This is a contractual role which will run through the end of 2026, with likelihood of extension into 2027.  
Responsibilities
  • Lead the end-to-end delivery of multiple AI, Machine Learning, and Generative AI programs across global business functions.
  • Drive AI initiatives from business intake through planning, delivery, deployment, production, and value realization.
  • Partner with Product Owners, AI Engineers, Data Scientists, MLOps teams, and business stakeholders to deliver scalable AI solutions.
  • Manage AI delivery roadmaps, dependencies, milestones, budgets, risks, and executive reporting across multiple concurrent initiatives.
  • Coordinate cross-functional teams spanning Business, Data Engineering, AI Engineering, Platform, Security, Governance, and Change Management.
  • Operate within Agile delivery methodologies including Scrum, SAFe, Kanban, and hybrid delivery models tailored for AI development.
  • Facilitate Agile ceremonies including Sprint Planning, Backlog Grooming, PI Planning, Reviews, Retrospectives, and Release Planning.
  • Manage AI-specific delivery backlogs including data readiness, feature engineering, model training, model validation, deployment readiness, and Responsible AI reviews.
  • Work within an enterprise AI Factory operating model, ensuring reusable assets, accelerators, governance frameworks, and shared AI platforms are leveraged effectively.
  • Drive AI Factory intake, prioritization, capacity planning, governance, and delivery throughput across multiple business initiatives.
  • Lead Value Teams focused on measurable business outcomes rather than traditional project milestones.
  • Champion Value-Based Delivery by aligning delivery priorities with defined business KPIs, measurable value hypotheses, ROI, and operational outcomes.
  • Monitor AI program health using delivery metrics including model cycle time, deployment velocity, adoption, business value realization, and delivery performance.
  • Manage AI delivery risks including data quality, model performance, governance, Responsible AI, regulatory compliance, and organizational readiness.
  • Support enterprise governance forums, AI steering committees, executive portfolio reviews, and business leadership reporting.
  • Coordinate User Acceptance Testing (UAT), production readiness, deployment planning, hypercare, and post-production stabilization.
  • Ensure AI initiatives comply with enterprise architecture standards, data governance policies, security requirements, and Responsible AI frameworks.
  • Build strong relationships with executive stakeholders while translating complex AI delivery concepts into business-focused outcomes.
  • Mentor delivery teams on Agile best practices specifically adapted to AI and Data Product delivery.


Requirements
  • 8+ years of Program or Project Management experience within enterprise technology organizations.
  • Minimum 2 years of recent (within the last 24 months) experience delivering AI, Machine Learning, or Generative AI programs.
  • Demonstrated experience managing enterprise AI delivery from concept through production deployment.
  • Proven experience delivering AI work products within Agile product delivery environments.
  • Strong understanding of AI/ML development lifecycle including:
    • Data Readiness
    • Feature Engineering
    • Model Development
    • Model Validation
    • Model Deployment
    • MLOps
    • Model Monitoring
    • Responsible AI
  • Hands-on experience operating within an AI Factory or similar centralized AI delivery model.
  • Experience leading Value Teams and delivering measurable business outcomes through Value-Based Delivery frameworks.
  • Strong understanding of Agile methodologies including Scrum, SAFe, Kanban, and Hybrid delivery.
  • Experience coordinating cross-functional teams including Product, Engineering, AI, Data Science, Security, Business, and Governance functions.
  • Experience managing enterprise portfolios, risks, dependencies, executive reporting, and delivery governance.
  • Strong stakeholder management and executive communication skills.
  • Experience supporting enterprise AI governance, Responsible AI practices, and compliance initiatives.
  • Ability to manage multiple concurrent AI programs within fast-paced enterprise environments.
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Business, Data Science, or a related discipline.
Preferred Qualifications
  • Healthcare, Pharma, or Life Sciences industry experience.
  • Experience delivering Generative AI (GenAI) initiatives.
  • Familiarity with Azure AI, Azure OpenAI, AWS SageMaker, Google Vertex AI, or similar enterprise AI platforms.
  • Experience with MLOps platforms and AI deployment pipelines.
  • Exposure to Data Mesh, Modern Data Platforms, or AI Platform Engineering.
  • PMP, SAFe, Scrum Master, Agile, or Product Management certifications.
  • Experience working with Responsible AI frameworks and AI governance boards.
  • Executive stakeholder engagement within global transformation programmes.