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Pytorch Developer Jobs (NOW HIRING)

AspireIT Solutions

Senior ML Ops Engineer

Dallas, TX · On-site

$103K - $142K/yr

Java (library/package management, algorithms), Python, TensorFlow / PyTorch * DevOps: CI/CD pipelines, Terraform * Containerization: Docker, Kubernetes Nice-to-Have Skills * Google Cloud Platform ML ...

Mercor

PyTorch MLOps Engineer - AI Specialist

New York, NY · Remote

$70 - $110/hr

PyTorch Expert Type: Contract Compensation: $70-$110/hour Location: Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to close knowledge gaps and improve ...

Saransh Inc

Agentic AI Developer

Jersey City, NJ · On-site

AI enabled chatbot Langchain TensorFlow Java PyTorch RASA Python AI Experience: 10-14 yrs Required ... programming languages such as Python, Java, or similar. • Experience with AI frameworks and ...

RAPID EAGLE INC

Python Developer

Charlotte, NC · On-site

$55 - $75/hr

Python Developer Onsite Role Charlotte NC Key Responsibilities • Build and maintain large-scale ... PyTorch and integrate them into production systems. • Develop and orchestrate ETL and ML ...

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Pytorch Developer information

What is a PyTorch Developer?

A PyTorch Developer is a software engineer or data scientist who specializes in using PyTorch, an open-source machine learning library, to build and deploy deep learning models. Their responsibilities typically include designing neural network architectures, training and evaluating models, and optimizing code for performance. PyTorch Developers work in fields such as artificial intelligence, computer vision, and natural language processing, collaborating with teams to solve complex problems using machine learning. They are proficient in Python and have a strong understanding of deep learning concepts. Additionally, they often contribute to research, development, and the deployment of AI solutions in production environments.

What are the key skills and qualifications needed to thrive as a Pytorch Developer, and why are they important?

To thrive as a Pytorch Developer, you need strong programming skills in Python, a solid grasp of machine learning concepts, and experience with deep learning frameworks—especially PyTorch itself. Familiarity with tools like CUDA, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected, along with knowledge of cloud platforms or relevant certifications. Problem-solving ability, effective collaboration, and clear communication are crucial soft skills for success in this role. These skills and qualities are vital for efficiently building, optimizing, and deploying machine learning models in real-world applications.

What is the difference between Pytorch Developer vs Machine Learning Engineer?

AspectPytorch DeveloperMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, experience with PyTorchBachelor's or higher in CS, data science, or related field, with ML experience
Work EnvironmentResearch labs, AI startups, tech companies focusing on deep learningTech companies, finance, healthcare, often involving deployment and scaling ML models
Industry UsagePrimarily in AI research and development teamsAcross industries implementing ML solutions in production

While both roles require knowledge of machine learning and experience with PyTorch, a Pytorch Developer mainly focuses on developing and optimizing deep learning models using PyTorch. A Machine Learning Engineer often has a broader scope, including deploying, maintaining, and scaling ML models across various platforms and industries.

What are some common challenges Pytorch Developers face when deploying machine learning models to production environments?

Pytorch Developers often encounter challenges when transitioning models from research to production, such as optimizing model performance for inference speed and memory usage, ensuring compatibility with deployment frameworks like TorchScript or ONNX, and managing dependencies across different systems. Additionally, integrating PyTorch models into existing software stacks and maintaining reproducibility can be complex. Collaborating closely with DevOps and data engineering teams is crucial to address these issues and ensure smooth deployment.
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Pytorch Developer jobs near you
Infographic showing various Pytorch 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.

Senior ML Ops Engineer

AspireIT Solutions

Dallas, TX • On-site

$103K - $142K/yr

Other

Posted 24 days ago

Job description

Role Summary

We are looking for a Senior ML Ops Engineer to own the full ML lifecycle — from model fine-tuning and training to production deployment and monitoring. This role is hands-on with GPUs, Terraform, and scalable ML systems. You'll work onsite 5 days a week in Dallas, TX.

Must-Have Skills
  • 10+ years of experience, with recent hands-on ML Ops work (within last 12 months)
  • End-to-end ML system design and implementation — from evaluation and statistical modeling to deployment and monitoring
  • ML model optimization: quantization, pruning, speculative decoding, etc.
  • System-level performance optimization — identifying and resolving bottlenecks
  • Strong programming: Java (library/package management, algorithms), Python, TensorFlow / PyTorch
  • DevOps: CI/CD pipelines, Terraform
  • Containerization: Docker, Kubernetes
Nice-to-Have Skills
  • Google Cloud Platform ML Tech Stack
  • Infrastructure as Code (IaC)
  • Big data technologies: Apache Spark, Hadoop
  • Awareness of ethical ML concerns (e.g., selection bias)
  • Model training & data analytics (anomalies, skew, discrepancies)
  • On-device ML system development
Key Responsibilities
  • Design and implement scalable, end-to-end ML systems in production environments
  • Fine-tune, train, and serve ML models with a focus on performance and reliability
  • Apply optimization techniques (quantization, pruning, speculative decoding) to improve model efficiency
  • Identify and resolve system-level bottlenecks in ML pipelines
  • Build and manage CI/CD pipelines and infrastructure using Terraform and containerization tools
  • Write clear technical documentation and communicate effectively across teams
  • Collaborate with cross-functional teams to ensure timely delivery and alignment on technical goals

Aspire IT Solutions is an equal opportunity employer.
All applicants must submit accurate information; misrepresentation may disqualify candidacy.
This role may require background checks/work authorization verification.
Rate/benefits are negotiable.
Aspire IT Solutions reserves the right to modify or withdraw this posting.

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