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Tensorflow Pytorch Jobs in Red Bank, NJ (NOW HIRING)

StradIT

AI Tech Lead - US

New York, NY · On-site

The ideal candidate has strong expertise in machine learning systems, data pipelines, and distributed computing, along with hands-on experience in Python and frameworks such as PyTorch, TensorFlow ...

Amazon

Professional Services III AMZ13646.13

New York, NY · On-site

$169K - $228K/yr

JavaScript frameworks such as D3 3) 3 years of experience with Python (NumPy, SciPy, Pandas, SciKitlearn, TensorFlow/PyTorch/MXNet) 4) 3 years of Machine Learning experience: supervised learning ...

DRW

AI Engineer

New York, NY · On-site

$175K - $300K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. * Experience with Large Language Models, including RAG and fine-tuning techniques. * Familiarity with compute ...

Triunity Software

AI Engineer

New York, NY · On-site

$100K - $110K/yr

  • Medical

Proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn * Experience with NLP, computer vision, or generative AI is a strong plus * Familiarity with cloud platforms ...

New

Cinder

AI Engineer

New York, NY · On-site

$200K - $250K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Fluency in Python and related AI/ML frameworks (TensorFlow, PyTorch, Keras, etc.) * Hands-on experience with LLMs and contemporary AI engineering patterns: RAG architectures, embedding models, vector ...

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All JobsTensorflow Pytorch JobsTensorflow Pytorch Jobs in Red Bank, NJ

Tensorflow Pytorch information

See Red Bank, NJ salary details

$38.5K

$126K

$201.7K

How much do tensorflow pytorch jobs pay per year?

As of Aug 17, 2026, the average yearly pay for tensorflow pytorch in Red Bank, NJ is $125,958.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,100.00 and $139,600.00 per year, depending on experience, location, and employer.

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What are the key skills and qualifications needed to thrive as a deep learning engineer specializing in TensorFlow and PyTorch?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

What job categories do people searching Tensorflow Pytorch jobs in Red Bank, NJ look for?

The top searched job categories for Tensorflow Pytorch jobs in Red Bank, NJ are:

What cities near Red Bank, NJ are hiring for Tensorflow Pytorch jobs?

Cities near Red Bank, NJ with the most Tensorflow Pytorch job openings:

Tensorflow Pytorch jobs near you
Infographic showing various Tensorflow Pytorch job openings in Red Bank, NJ as of June 2026, with employment types broken down into 1% Internship, 86% Full Time, 7% Part Time, and 6% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $125,958 per year, or $60.6 per hour.

AI Tech Lead - US

StradIT

New York, NY • On-site

Full-time

Re-posted 3 days ago

Job description

We are seeking an experienced AI Engineering Lead to design, build, and scale advanced AI systems from model development through production deployment. The role involves leading the architecture of machine learning solutions, defining engineering best practices, and ensuring robust, reliable, and scalable AI workflows.
The ideal candidate has strong expertise in machine learning systems, data pipelines, and distributed computing, along with hands-on experience in Python and frameworks such as PyTorch, TensorFlow, or JAX. This role requires experience with cloud platforms (AWS, GCP, or Azure), containerization tools like Docker and Kubernetes, and familiarity with MLOps practices including model versioning, deployment, and monitoring.
You will collaborate closely with research, product, and engineering teams to deliver high-impact AI solutions, while also mentoring engineers and driving technical excellence across the organization. Strong leadership and communication skills are essential, along with a proven ability to bridge research and production environments.
This is a hybrid position for candidates located in NY/NJ that can commute to the office 4 days a week.
Required Qualifications
  • Lead the architecture and implementation of AI systems, from model development to deployment.
  • Define engineering best practices for AI workflows, model lifecycle, and system reliability.
  • 8+ years of experience in software engineering, including 2+ years leading or mentoring teams.
  • Strong background in machine learning systems, data pipelines, or distributed computing.
  • Proficiency in Python (preferred), plus experience with frameworks such as PyTorch, TensorFlow, or JAX.
  • Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
  • Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.).
  • Strong understanding of model deployment, versioning, and monitoring.
  • Excellent communication skills and ability to collaborate with research and product stakeholders.

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