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Remote Deep Learning Jobs in Trenton, NJ (NOW HIRING)

GATE 1 LTD

A.I. Manager

Fort Washington, PA · On-site +1

Build and lead a remote AI team, including recruiting, mentoring, and performance management ... Guide model development, including machine learning, deep learning, NLP, and generative AI ...

GATE 1 LTD

A.I. Manager

PA · On-site +1

Build and lead a remote AI team, including recruiting, mentoring, and performance management ... Guide model development, including machine learning, deep learning, NLP, and generative AI ...

Snelling Services Princeton NJ

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Executive Vice President / Business Development

Princeton, NJ · Remote

$250K - $300K/yr

... Learning & Development & Medical Affairs Remote | $250,000-$300,000+ Base Salary + Commission ... This is an exciting opportunity for a strategic, relationship-driven sales leader with deep ...

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All JobsDeep Learning JobsRemote Deep Learning JobsRemote Deep Learning Jobs in Trenton, NJ

Remote Deep Learning information

See Trenton, NJ salary details

$11K

$84.1K

$140.4K

How much do remote deep learning jobs pay per year?

As of Aug 20, 2026, the average yearly pay for remote deep learning in Trenton, NJ is $84,117.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,200.00 and $139,400.00 per year, depending on experience, location, and employer.

What is a remote deep learning engineer?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What skills and qualifications are needed to thrive as a remote deep learning engineer?

To thrive as a Remote Deep Learning Engineer, you need strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.

What are common challenges faced by remote deep learning engineers, and how can they be addressed?

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

What is the difference between Remote Deep Learning vs Remote Machine Learning Engineer?

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

What job categories do people searching Remote Deep Learning jobs in Trenton, NJ look for?

The top searched job categories for Remote Deep Learning jobs in Trenton, NJ are:

What cities near Trenton, NJ are hiring for Remote Deep Learning jobs?

Cities near Trenton, NJ with the most Remote Deep Learning job openings:

Remote Deep Learning jobs near you

AI/ML Engineer - LLM & AI Harness Engineering - 100% Remote US

WilsonCTS

Middletown, PA • On-site, Remote

$125/hr

Full-time, Part-time, Contractor

Posted 5 days ago

Job description

AI/ML Engineer - LLM & AI Harness Engineering

Location: 100% Remote - United States
Schedule: Monday-Friday, 8:00 AM-5:00 PM ET
Duration: 12-Month Contract
Compensation: Up to $125/hour
Hours: Full-time preferred; part-time may be considered for the right candidate

About the Opportunity

A leading global technology and engineering company is seeking an experienced AI/ML Engineer to join its Digital Data Networks organization and help build practical AI solutions that accelerate engineering productivity, technical data analysis, and decision-making.

This is a highly hands-on role focused on AI/LLM harness engineering. You will build Python-based solutions around existing AI models, incorporating LLMs, Retrieval-Augmented Generation (RAG), AI agents, tool calling, structured workflows, evaluation, and guardrails.

The ideal candidate combines strong AI/ML engineering skills with the ability to understand and work with complex technical and engineering data.

What You'll Do

  • Develop and validate Python-based AI/ML and LLM workflows for engineering analysis, technical data processing, automation, and decision support.

  • Build model training and validation pipelines using open datasets and adapt approaches for engineering datasets such as s-parameters, VNA, simulation, test, and other measurement data.

  • Apply machine learning and deep learning techniques, including neural networks, CNNs, and LSTM/recurrent models, to practical engineering challenges.

  • Develop LLM workflows for data parsing, summarization, extraction, classification, and structured outputs using local or hosted AI models.

  • Design and implement RAG solutions that ground AI responses in trusted engineering documents, datasets, and approved knowledge sources.

  • Build AI-agent and LLM harness workflows incorporating task routing, tool calling, workflow orchestration, evaluation, and guardrails.

  • Develop or integrate custom tools that allow AI workflows to interact with engineering and technical data sources.

  • Collaborate with signal integrity, product development, testing, manufacturing, and operations teams to identify opportunities for AI automation and decision support.

  • Translate technical requirements into reliable, reusable AI workflows and prototypes.

  • Document AI workflows, assumptions, validation approaches, limitations, and recommended next steps.

  • Evaluate AI-generated results, identify limitations or risks, and make data-driven recommendations for improvement.

Required Qualifications

  • Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related technical discipline. Master's degree is a plus.

  • Strong hands-on experience with Python for AI/ML development, data processing, model training, validation, and automation.

  • Solid understanding of machine learning and deep learning, including neural networks, CNNs, and LSTM/recurrent architectures.

  • Experience with AI/ML frameworks such as PyTorch, TensorFlow, or equivalent.

  • Understanding of GPU-enabled AI/ML development and CUDA, particularly in NVIDIA environments.

  • Practical knowledge of Large Language Models (LLMs) and experience working with open-source and/or commercial AI models.

  • Experience with local LLM environments or model-serving tools such as Ollama, LM Studio, llama.cpp, or equivalent.

  • Experience with Hugging Face, LangChain, or similar AI/LLM frameworks.

  • Strong understanding of Retrieval-Augmented Generation (RAG) and experience implementing RAG-based workflows.

  • Ability to design AI-agent/harness architectures incorporating RAG, tool calling, workflow orchestration, evaluation, guardrails, and external data sources.

  • Strong analytical and problem-solving abilities with a focus on validating AI outputs and understanding model limitations.

  • Excellent communication skills and the ability to explain AI concepts and technical tradeoffs to engineering stakeholders.

  • Ability to work independently, learn quickly, and collaborate effectively within a global technical organization.

Nice-to-Have Experience

  • Experience applying AI/ML or LLMs to engineering, signal-integrity, measurement, simulation, test, or product-development datasets.

  • Experience developing custom AI tools for engineering measurement, simulation, test, or product-development workflows.

  • Experience using Generative AI to support product design, engineering parameter optimization, or design iteration.

  • Experience using AI to identify product defects, performance issues, root causes, and corrective actions.

  • Experience with AWS-based AI/data environments, including databases, queues, notebooks, or related infrastructure.

  • Strong experience with Python/Jupyter notebooks for rapid prototyping and technical demonstrations.

  • Experience evaluating user or engineering performance with and without AI assistance.

  • Understanding of GPU resource planning and compute constraints impacting AI/ML development.

  • Hands-on experience with LLM fine-tuning, domain-specific model adaptation, training-data development, model serving, or GPU optimization.

  • Experience working in high-speed interconnect, cable assembly, signal integrity, or related engineering/product-development environments.

Why This Role?

This is an opportunity to work at the intersection of AI, LLMs, software engineering, and advanced engineering applications. You'll have the opportunity to move beyond experimentation and build practical AI systems that can be used by technical teams to analyze data, automate workflows, improve engineering decisions, and accelerate product development.

Apply