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Temporary Meta Machine Learning Jobs in New Jersey

Virtusa Corporation

AI Solutions Architect

Piscataway, NJ · On-site

$64.25 - $84.50/hr

... machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability ... and Meta (LLaMA). • Develop and deploy AI solutions using Python, Java, and frameworks like ...

Monitise

AI Engineer

Berkeley Heights, NJ · On-site

As an AI Engineer you will apply advanced machine learning and statistical techniques to detect ... Individuals with temporary visas including, but not limited to, F-1 (OPT, CPT, STEM), H-1B, H-2, or ...

Rider University

Electrician - Temporary

Lawrenceville, NJ · On-site

$26/hr

... machinery and circuits. Individual will install and maintain electrical systems for the buildings ... Rider University is committed to fostering an inclusive, vibrant living and learning community that ...

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Temporary Meta Machine Learning information

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 are the most commonly searched types of Meta Machine Learning jobs in New Jersey?

The most popular types of Meta Machine Learning jobs in New Jersey are:

What are popular job titles related to Temporary Meta Machine Learning jobs in New Jersey?

For Temporary Meta Machine Learning jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Temporary Meta Machine Learning jobs in New Jersey look for?

The top searched job categories for Temporary Meta Machine Learning jobs in New Jersey are:

What cities in New Jersey are hiring for Temporary Meta Machine Learning jobs?

Cities in New Jersey with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you

Applied Artificial Intelligence/ Machine Learning Lead - Vice President

JPMorgan Chase & Co.

Jersey City, NJ • On-site

$250 - $450/hr

Other

Re-posted 2 days ago

JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz

77th of 174 rated banks

Job description

As an Applied AI/ML Vice President within Global Private Bank, you will lead the design and build of agentic AI systems that execute reliable business workflows end-to-end. You will bring deep expertise in agent architectures—including memory, state, and context management; loop engineering; tool orchestration; and spec-driven development to deliver safe, observable, and high-quality solutions. You will stay close to cutting-edge research, translating advances in LLMs, agent frameworks, reinforcement learning, knowledge graphs, retrieval, and self-improving systems into practical capabilities. You’ll thrive in a highly collaborative environment, partnering with business, technologists, and control partners to shape requirements, controls, and success metrics.

Job Responsibilities:
  • Develop advanced agentic AI solutions across NLP, speech analytics, time series, reinforcement learning, and recommendation systems.
  • Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs—spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
  • Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
  • Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
  • Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
  • Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
  • Coach and mentor AIML team members, setting a high bar for engineering rigor and research depth.
Required qualifications, capabilities, and skills:
  • PhD in a quantitative discipline (e.g., CS/EE/Math/OR/Optimization/Data Science) or equivalent industry/research experience (e.g., 3+ years with PhD-equivalent depth; or MS with 5+ years).
  • Demonstrated expertise building agentic AI systems, including several of: memory/state/context management, tool orchestration and workflow reliability patterns, loop engineering, spec-driven development, and prompt/skill instruction optimization.
  • Strong hands-on experience with ML/DL methods and toolkits (e.g., PyTorch/TensorFlow plus core Python data/ML stack).
  • Ability to design experiments and evaluation frameworks with metrics aligned to business outcomes (quality, reliability, latency, cost, safety).
  • Experience with scalable data and model workflows (training and/or inference) and strong software engineering practices.
  • strong communication skills to explain technical concepts to both technical and business audiences.
Preferred qualifications, capabilities, and skills:
  • Knowledge in search/ranking, reinforcement learning, or meta-learning (especially for agent routing, policies, and self-improvement).
  • Experience with knowledge graphs, entity resolution, and ontology design.
  • Experience with A/B experimentation and metric-driven product development; CI pipelines and unit/integration testing.
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