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Hourly Remote Tensorflow Developer Jobs (NOW HIRING)

Huzzle

AI Engineer - Remote

You will work closely with product, engineering, and data teams to design intelligent systems ... Strong experience with Python and AI/ML libraries such as TensorFlow, PyTorch, Scikit-learn, or ...

ACS Consultancy Services

AI/ML Engineer

Albany, NY · Remote

Remote We are currently seeking candidates who meet the following qualifications. Key ... Build and optimize data pipelines and ML workflows using frameworks like TensorFlow, PyTorch ...

Gnostech LLC

Software Engineer (US-Remote)

OR · Remote

Collaborate with cross-functional teams including DevOps, cybersecurity, QA, and product ... Experience with AI/ML technologies, including machine learning frameworks (TensorFlow, PyTorch) or ...

Bean Hr Consulting

GenAI Architect

Manhattan, NY · Remote

$69.50 - $91.50/hr

Remote About the Role We are seeking an experienced Generative AI Architect to lead end-to-end AI ... engineering, and team leadership. Strong experience in GenAI, NLP, CV, Python, TensorFlow/JAX, and ...

Zortech Solutions

MLOps Engineer

Bellevue, WA · Remote

Bellevue, WA Remote Work100% Primary SkillsAWS Cloud Formation * MLOps Engineer to work on AWS ... as Keras, Tensorflow, PyTorch, HuggingFace Transformers and libraries (like scikit-learn, etc ...

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How much do hourly remote tensorflow developer jobs pay per hour?

As of May 30, 2026, the average hourly pay for hourly remote tensorflow developer in the United States is $52.84, according to ZipRecruiter salary data. Most workers in this role earn between $40.38 and $64.66 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Hourly Remote TensorFlow Developer, and why are they important?

To thrive as an Hourly Remote TensorFlow Developer, you need a solid background in computer science, proficiency in Python, and hands-on experience with machine learning frameworks like TensorFlow. Familiarity with version control systems (e.g., Git), cloud platforms (such as AWS or GCP), and TensorFlow-specific certifications can be highly beneficial. Strong problem-solving, self-motivation, and clear communication skills are crucial for effective remote collaboration and project delivery. These competencies enable developers to efficiently build, deploy, and maintain machine learning models while meeting client requirements in a remote work environment.

What challenges can Hourly Remote TensorFlow Developers expect when collaborating with distributed teams?

As an Hourly Remote TensorFlow Developer, you’ll often collaborate with cross-functional teams spread across different time zones and backgrounds. One common challenge is maintaining clear communication, especially when troubleshooting complex machine learning models or integrating code. It’s important to document your work thoroughly and leverage asynchronous communication tools to keep everyone aligned. Regular check-ins and code reviews also help to ensure smooth collaboration and high-quality deliverables.

What is an Hourly Remote Tensorflow Developer?

An Hourly Remote Tensorflow Developer is a software professional who specializes in building and deploying machine learning models using TensorFlow, an open-source AI framework. They work on a contract or freelance basis, charging by the hour, and perform their tasks entirely remotely. Their responsibilities often include designing neural networks, data preprocessing, model training, and optimization to solve various business problems. By working remotely, they can collaborate with teams and clients from anywhere in the world, providing flexible and scalable machine learning solutions.
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Hourly Remote Tensorflow Developer jobs near you
Infographic showing various Hourly Remote Tensorflow Developer job openings in the United States as of May 2026, with employment types broken down into 1% Locum Tenens, 11% Full Time, 81% Part Time, 6% Contract, and 1% Nights. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution, with an average salary of $109,905 per year, or $52.8 per hour.
GenAI Engineer - Remote

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Posted 11 days ago

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs—particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

  • Enables domain-specific fine-tuning of models to client's unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:

  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to client's domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:

  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content