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Ai Prompt Engineer Jobs in Edison, NJ (NOW HIRING)

Everstar Inc

Founding AI Engineer

New York, NY · On-site

Not a prompt engineer. This is a production-first role. You'll own the AI stack end-to-end -from eval frameworks to fine-tuning pipelines to agent orchestration. But you'll also push the boundaries ...

Millennium Management LLC

AI Scientist

New York, NY · On-site

$175K - $250K/yr

Hands-on experience building and deploying AI and machine learning solutions, ideally including LLMs, generative AI, prompt engineering, RAG, fine-tuning, and agentic systems. * Strong Python ...

InterSources Inc

Gen AI Engineer

Edison, NJ · On-site

The role involves developing AI workflows, prompt engineering, and maintaining AI models across various environments. Responsibilities : • AI workflow development • Prompt engineering • ...

Alice

Freelancer - AI CBRN Experts

New York, NY · Remote

Knowledge of AI prompt engineering concepts and practices, including adversarial prompt writing All applicants will be required to complete a screening exam and background check.

Alice

Freelancer - AI CBRN Experts

New York, NY · On-site +1

Knowledge of AI prompt engineering concepts and practices, including adversarial prompt writing All applicants will be required to complete a screening exam and background check.

Future Secure AI

Sr. AI/ML Engineer, Platform

Manhattan, NY · On-site

$115K - $158K/yr

Future Secure AI is building innovative solutions at the frontier of AI, tackling real-world ... for prompt engineering, tool use, streaming, and token management. • Distributed Systems ...

Tetra Tech, Inc.

AI Lead (Hybrid)

Parsippany, NJ · On-site

$167K/yr

Prompt Engineering * Retrieval-Augmented Generation (RAG) * AI Governance * Experience with Microsoft Azure AI, Microsoft Copilot, Power Platform, OpenAI technologies, or comparable enterprise AI ...

Northern Base

AI/LLM Engineer

New York, NY · On-site

Prompt Engineering Collaboration * Async Python Programming * API & Microservices Development * Scalable AI Application Architecture * LLM Guardrails & Safety Mechanisms * Responsible AI & Edge Case ...

Virtusa Corporation

Senior Architect

New York, NY · On-site

Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, prompt engineering, embeddings, vector databases, and AI evaluation methodologies. Experience ...

Socket.dev

AI & Data Engineer

Lawrence Township, NJ · On-site

$140 - $190/hr

Python, AI/ML & Prompt Engineering * Develop and maintain Python-based automation scripts and tools to streamline website setup, content migration, metadata enrichment, QA validation, and reporting ...

Giftogram

AI Operations Manager

Whippany, NJ · On-site

$70K - $90K/yr

Familiarity with AI prompt engineering or building Claude/GPT-powered automations * Prior exposure to B2B SaaS, rewards, incentives, or a revenue-driven business What We Offer * Competitive base ...

Giftogram

AI Operations Manager

Whippany, NJ · On-site

Familiarity with AI prompt engineering or building Claude/GPT-powered automations * Prior exposure to B2B SaaS, rewards, incentives, or a revenue-driven business What We Offer * Competitive base ...

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Ai Prompt Engineer information

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How much do ai prompt engineer jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for ai prompt engineer in Edison, NJ is $55.52, according to ZipRecruiter salary data. Most workers in this role earn between $44.81 and $64.47 per hour, depending on experience, location, and employer.

What is an AI prompt engineer?

An AI Prompt Engineer designs, tests, and optimizes prompts to improve the performance of AI language models. They ensure that AI-generated responses align with desired outcomes by refining input prompts and analyzing model behavior. This role requires a mix of technical skills, creativity, and an understanding of natural language processing (NLP). Prompt engineers often collaborate with developers, data scientists, and product teams to enhance AI interactions.

What does an AI prompt engineer do?

As an AI Prompt Engineer, your day-to-day work often involves designing, testing, and refining prompts to improve the performance and accuracy of AI models for various applications. You'll frequently collaborate with data scientists, product managers, and software developers to understand requirements and integrate AI solutions effectively. Analyzing prompt outputs, troubleshooting issues, and documenting best practices are integral parts of the role. This position offers a dynamic and intellectually stimulating environment where continual learning and innovation are encouraged.

What are the key skills and qualifications needed to thrive as an AI prompt engineer?

To thrive as an AI Prompt Engineer, a solid background in natural language processing, programming (such as Python), and understanding of machine learning concepts is essential, typically supported by a degree in computer science or a related field. Experience with AI frameworks (like OpenAI's APIs), prompt engineering tools, and familiarity with cloud platforms are highly valued and may be supplemented by certifications in AI or data science. Strong analytical thinking, creativity, and excellent communication skills allow for designing effective prompts and collaborating with technical and non-technical stakeholders. These skills and qualities ensure the development of high-quality AI solutions that meet user needs and maximize the effectiveness of language models.

How do I become an AI prompt engineer?

To become an AI prompt engineer, develop strong skills in natural language processing, machine learning, and programming languages like Python. Gain experience with AI models such as GPT and learn to craft effective prompts through practice and understanding of model behavior. Relevant certifications, online courses, and familiarity with AI tools can also enhance your qualifications.

How much do AI prompt engineers make?

AI prompt engineers typically earn between $70,000 and $130,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in machine learning and natural language processing can command higher salaries. Many positions also offer benefits such as flexible schedules and opportunities for professional development.

What is the job of an AI Prompt Engineer?

An AI Prompt Engineer designs and optimizes prompts to improve the performance of AI language models. They analyze model responses, experiment with prompt structures, and use tools like natural language processing to ensure accurate and relevant outputs, often working with machine learning frameworks and data annotation techniques.

What are the most commonly searched types of Ai Prompt Engineer jobs in Edison, NJ?

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For Ai Prompt Engineer jobs in Edison, NJ, the most frequently searched job titles are:

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Ai Prompt Engineer jobs near you
Infographic showing various Ai Prompt Engineer job openings in Edison, NJ as of August 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 100% In-person job distribution, with an average salary of $115,485 per year, or $55.5 per hour.

Founding AI Engineer

Everstar Inc

New York, NY • On-site

Full-time

Medical, Dental, Vision

Re-posted 15 days ago

Key responsibilities

  • Build and ship production AI systems, including evaluation frameworks, fine-tuning pipelines, and agent orchestration.

  • Develop AI models and simulations for nuclear deployment, weather forecasting, safety analysis, and operational monitoring.

  • Design and implement infrastructure for benchmarking, model quality assessment, and domain-specific AI applications.

Job description

Founding AI Engineer (AI + Production)
New York City (5 days on-site) • Top of market + equity + benefits
TL;DR: Build AI that accelerates nuclear deployment. Own AI production from evals to fine-tuning. Push the frontier on physics models, world models, and AI-accelerated simulations. High-leverage IC role with founding-level impact.
The Mission
Everstar builds the intelligence layer that makes nuclear power actually deployable-collapsing regulatory and manufacturing timelines from years to months. Gordian already powers engineering and compliance work for utilities, advanced reactor companies, and hyperscalers. We pair deep nuclear domain expertise with frontier AI and move with startup speed.
Now we need a Founding AI Engineer to turn research breakthroughs into production systems that ship-and push beyond LLMs into physics-informed AI, world models, and simulation acceleration.
You'll be joining the Apollo Team of Nuclear. You'll build alongside engineers from Tesla, SpaceX, Lockheed Martin, Google, and Microsoft. You'll learn from nuclear and national security experts who cut their teeth at the Nuclear Regulatory Commission, CIA, and NuScale.
The Role (reporting to the CEO)
Not a researcher. Not a prompt engineer. This is a production-first role.
You'll own the AI stack end-to-end-from eval frameworks to fine-tuning pipelines to agent orchestration. But you'll also push the boundaries of what AI can do for nuclear: AI-accelerated weather simulations, design safety analyses powered by physics models, and world model applications that transform nuclear operations.
Think 70% building production systems / 30% frontier R&D with access to Microsoft and NVIDIA's latest tools through our first-party partnerships and a large AI research budget to experiment aggressively.
Most weeks you'll be shipping new model capabilities, debugging eval failures, and scaling inference-then immediately applying what you learned to the next sprint. Some weeks you'll be prototyping physics-informed models, running GPU-accelerated simulations, or collaborating directly with NVIDIA and Microsoft researchers.
You will:
  • Build production AI agents: power Gordian Search, Research, and Compose with outputs that are truthful, complete, and auditable-because in nuclear, "mostly right" isn't good enough.
  • Design eval infrastructure: create benchmarking suites that catch regressions before customers do; instrument quality metrics that actually matter.
  • Own fine-tuning pipelines: generate synthetic data, run ablations, and ship domain-adapted models that outperform off-the-shelf LLMs on nuclear regulatory tasks.
  • Push the frontier (R&D):
    • AI-accelerated weather simulations for site qualification and environmental impact assessments-replacing months of modeling with hours
    • Physics-informed design safety analyses using world models that reason about thermal hydraulics, neutronics, and structural integrity
    • Vision + physics models for automated document analysis, construction monitoring, and operational anomaly detection
    • Agentic workflows that compound over time, learning from each regulatory submission to improve the next
  • Leverage NVIDIA partnership: work directly with NVIDIA's research team to access cutting-edge tools (NeMo, Modulus, Omniverse) and contribute to the future of AI for critical infrastructure
  • Set technical direction: you're early enough to shape how we think about model selection, prompt design, guardrails, physics-AI integration, and the entire ML ops stack
  • Mentor and lead: as the team scales, you'll hire and guide other AI engineers-but first, you'll prove the playbook yourself

A sample week: debug why Research citations dropped 8%; ship new fine-tuned model for compliance drafting; design eval suite for multi-document reasoning; prototype physics-informed model for thermal analysis; pair with fullstack engineer to optimize inference latency; attend NVIDIA collaboration session on world models; read three ML papers and implement one idea.
What You've Done
  • 3-8 years building production ML/LLM systems-RAG, fine-tuning, evals, agent orchestration. You've shipped models that users depend on daily.
  • Mastery of the stack: Hugging Face, LangChain, vector databases, prompt engineering, and modern LLM ops. You know when to use off-the-shelf and when to build custom.
  • Rigor with evals: you've designed benchmark suites, tracked model quality over time, and know how to measure what matters (not just what's easy).
  • Leadership DNA: you've owned outcomes, not just tasks. You've set technical direction, mentored teammates, or led cross-functional projects.
  • Bonus points:
    • Experience with physics-informed neural networks, scientific computing, or simulation acceleration
    • Published research in ML/AI, contributions to open-source ML frameworks
    • Deep familiarity with NVIDIA tools (NeMo, Modulus, CUDA optimization)
    • You're the person who reads Arxiv papers on weekends and immediately wants to implement them
    • Background in physics, engineering, or computational science

No nuclear background required-only the hunger to build AI that matters and push the boundaries of what AI can do for physical systems.
Who You're Building For
This isn't benchmarks for benchmarks' sake. Your models will directly help:
  • Nuclear operators keeping 20% of U.S. electricity safe and reliable
  • Advanced reactor developers navigating regulatory approval for next-gen designs-and using AI-accelerated simulations to optimize designs in days, not months
  • Licensing teams drafting safety analyses that take months today, hours tomorrow-powered by physics models that understand first principles
  • Site qualification teams running environmental and weather analyses that currently require expensive consultants and 6+ month timelines

And the second-order effects matter even more:
  • Nuclear unlocks the energy needed for AGI/ASI-advanced AI requires unprecedented power.
  • AI accelerates nuclear deployment-breaking the regulatory bottleneck that's held back clean energy for decades.
  • The tokens you generate translate into safer infrastructure and a livable planet.

What's at Stake
  • If we succeed: We unlock nuclear at scale, power the AI revolution with clean energy, and collapse licensing timelines from years to months. The models you build help humanity leap toward AGI on a sustainable foundation. Your physics-informed AI becomes the standard for how critical infrastructure is designed and operated.
  • ❄ If we fail: Nuclear stays bottlenecked in decades-old processes, AI's energy demand outpaces clean supply, and we miss the window to align technological progress with climate survival. The frontier AI capabilities remain academic curiosities instead of deployment accelerators.

What Success Looks Like (90 days)
  • Shipped ≥3 major model improvements to production (better evals, new fine-tuned model, or agent capability).
  • Eval framework is instrumented and running continuously; you catch quality regressions before customers report them.
  • Inference latency reduced ≥30% or accuracy improved ≥15% on key benchmarks.
  • Prototype ≥1 frontier capability (physics model for safety analysis, weather simulation acceleration, or world model application) that shows clear customer value.
  • You've set the technical roadmap for AI engineering and the team trusts your judgment.
  • At least one system you built (eval suite, fine-tuning pipeline, or agent orchestration) is now core infrastructure the company depends on.

Resources at Your Disposal
  • NVIDIA & Microsoft first-party partnership: Direct access to Microsoft & NVIDIA research team, early access to new tools (NeMo, Modulus, Omniverse), and collaboration on frontier AI applications
  • Large AI research budget: Aggressive compute allocation for training runs, experiments, and frontier R&D-no need to beg for GPU credits
  • Latest NVIDIA hardware: Access to H100s, GH200s, and future architectures as they become available
  • World-class team: Work alongside nuclear domain experts, AI researchers, and engineers who've shipped at SpaceX and top startups

Growth Path
Strong founding AI engineers typically grow into Head of AI/ML, AI Research Lead, or CTO-track roles as the company scales. The frontier R&D component opens paths toward Chief Scientist or VP of Applied Research as we expand into physics-AI and world models.
First, you'll prove you can own the entire LLM stack and ship production systems that matter.
Why Everstar
  • Work with the best: high‑caliber, wartime team that builds things that scale
  • Build shit that matters, accelerating nuclear energy and shaping the AI future
  • Large AI research budget for compute, conferences, and experimentation.
  • Top of market base + meaningful equity in a fast-growing company; standard benefits (health/dental/vision, FSA, wellness stipend).
  • IRL in NYC (midtown/Bryant Park). Occasional travel to client sites, Microsoft & NVIDIA offices, or ML conferences.

How to Apply (show, don't tell)
Submit application with:
  1. Resume AND LinkedIn profile
  2. GitHub or portfolio: show us something you built (open-source contributions, side projects, or production work you're proud of)
  3. 200 words: "What excites you most about building AI for nuclear deployment?"
  4. 150 words: "Describe a production ML system you owned. What were the hardest technical tradeoffs and how did you resolve them?"
  5. Bonus (optional): If you have experience with physics-informed AI, simulation acceleration, or scientific computing, share a brief example of work in this domain.

We respond to strong submissions within one week.
Let's build.

Apply