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Remote Tensorflow Developer Jobs (NOW HIRING)

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 ...

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 ...

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How much do remote tensorflow developer jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for 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 is a Remote Tensorflow Developer job?

A Remote TensorFlow Developer job involves designing, implementing, and optimizing machine learning models using TensorFlow while working from a remote location. Developers in this role typically collaborate with data scientists, engineers, and product teams to build AI-driven applications and improve model performance. Responsibilities may include data preprocessing, model training, deployment, and fine-tuning for scalability and efficiency. Strong knowledge of deep learning, neural networks, and cloud platforms is often required.

What are the key skills and qualifications needed to thrive in the Remote Tensorflow Developer position, and why are they important?

To thrive as a Remote Tensorflow Developer, you need deep knowledge of machine learning concepts, strong proficiency in Python programming, and hands-on experience with Tensorflow framework. Experience with cloud platforms (such as AWS, GCP, or Azure), model deployment, and relevant Tensorflow Developer certification are highly valuable. Excellent problem-solving abilities, self-motivation, and effective remote communication skills help developers stand out. These qualities are crucial for building robust machine learning solutions, efficiently collaborating with distributed teams, and delivering high-impact results in a remote setting.

What are some typical challenges faced by Remote Tensorflow Developers, and how are these addressed?

Remote Tensorflow Developers often face challenges such as collaborating across different time zones, managing large datasets, and keeping up with rapidly changing machine learning technologies. These challenges are typically addressed through robust communication tools (like Slack or Zoom), using version control systems for code collaboration, and adopting efficient cloud-based workflows for data and model sharing. Teams may also conduct regular virtual stand-ups and knowledge-sharing sessions to stay aligned on projects and share learnings. Engaging in continuous learning and attending online workshops or conferences also helps remote developers stay updated and effective in their roles.

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Remote Tensorflow Developer jobs near you
Infographic showing various Remote Tensorflow Developer job openings in the United States as of July 2026, with employment types broken down into 85% Full Time, 3% Part Time, 1% Temporary, and 11% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% 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

Re-posted 26 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