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Pytorch Contract Jobs (NOW HIRING)

Saransh Inc

Agentic AI Developer

Jersey City, NJ · On-site

Contract Job Summary: As an AI Agent Developer, you will design, develop, and deploy intelligent AI ... AI enabled chatbot Langchain TensorFlow Java PyTorch RASA Python AI Experience: 10-14 yrs Required ...

Centraprise

ML Engineer

Minneapolis, MN · On-site

Collaborate with Data Engineering on feature pipelines and data contracts. * Own production health ... Python, TensorFlow, PyTorch, Docker, REST APIs

Saransh Inc

Lead AI/ML Developer

New York, NY · On-site

$64.50 - $84.50/hr

Contract Responsibilities: * Design and implement machine learning models and algorithms for ... Tensor, PyTorch * AWS, Azure * Pandas, Numpy * CI/CD

MatchPoint Solutions

Embedded AI Engineer

Sunnyvale, CA · On-site

$156K - $206K/yr

Sunnyvale, CA Employment Type: 6+ Month Extendable Contract Pay Range: USD 70-80/HR - Role Overview ... Key Responsibilities: • Validate PyTorch-based LLMs on company-specific AI processors using CUDA ...

Staffingine LLC

AI Engineer

Phoenix, AZ · On-site

Contract * Develop and implement AI solutions using Python and AI frameworks such as Langgraph and ... XGBoost and PyTorch for model development and training Design and optimize data processing ...

Purple Drive Technologies

Data Scientist

Plano, TX · On-site

Contract Experience Required: 8-10+ Years Position Overview We are seeking experienced Data ... Apply machine learning algorithms using libraries such as Scikit-learn, TensorFlow, or PyTorch

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Pytorch Contract information

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How much do pytorch contract jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for pytorch contract in the United States is $26.18, according to ZipRecruiter salary data. Most workers in this role earn between $20.19 and $28.61 per hour, depending on experience, location, and employer.

What are some common challenges faced by PyTorch contractors when working on short-term projects?

PyTorch contractors often encounter challenges such as understanding legacy codebases quickly, aligning with existing team workflows, and meeting tight project deadlines. Since contracts are typically short-term, there is limited time to ramp up and become familiar with both the technical stack and the specific business goals. Effective communication and adaptability are essential, as you may need to collaborate with data scientists, engineers, and project managers to deliver results efficiently. Staying organized and proactively seeking clarification can help you overcome these common hurdles and succeed in a contract role.

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

To thrive as a PyTorch Contract Developer, you need strong proficiency in Python programming, deep learning concepts, and hands-on experience with the PyTorch framework, often supported by a degree in computer science or a related field. Familiarity with version control systems like Git, cloud platforms (e.g., AWS, Azure), and tools such as Jupyter Notebooks or Docker is typically required. Problem-solving abilities, effective communication, and adaptability to evolving project requirements are important soft skills. These skills ensure the delivery of robust machine learning solutions and seamless collaboration within dynamic project environments.

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

AspectPytorch ContractMachine Learning Engineer
Required CredentialsExperience with PyTorch, Python, and deep learning frameworksDegree in CS, Data Science, or related field; experience with ML frameworks including PyTorch
Work EnvironmentProject-based, often freelance or temporary roles in AI/ML projects
Employer & Industry UsageTech companies, startups, research institutions focusing on AI development
Search & Comparison IntentLooking for short-term or contract-based PyTorch roles

While a Pytorch Contract typically refers to a temporary or project-specific role involving PyTorch, a Machine Learning Engineer is a full-time professional responsible for designing, developing, and deploying ML models, often using PyTorch among other tools. The contract role is more focused on specific tasks within a project, whereas the engineer role encompasses broader responsibilities in AI development.

What is a PyTorch contract job?

A PyTorch contract job is a temporary position where professionals work on projects that involve building, training, or deploying machine learning models using the PyTorch framework. These contracts are typically for a fixed duration or until the completion of a specific project. PyTorch contractors may be tasked with developing deep learning solutions, optimizing neural networks, or integrating models into production environments. Such roles are common in industries like tech, healthcare, and finance, where deep learning expertise is in high demand. Contractors often work remotely or on-site, collaborating with data science or engineering teams.
What are the most commonly searched types of Pytorch jobs? The most popular types of Pytorch jobs are:
Infographic showing various Pytorch Contract job openings in the United States as of May 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 67% In-person, 8% Hybrid, and 25% Remote job distribution, with an average salary of $54,445 per year, or $26.2 per hour.

PyTorch with Triton performance Engineer

VDart, Inc.

Bellevue, WA

Other

Posted 2 days ago

Job description

PyTorch with Triton performance Engineer

Bellevue, WA (Onsite)

Contract / FTE


Job Summary
      Design and implement high intensity stress workloads using PyTorch and Triton to identify performance bottlenecks and improve platform stability and maturity

Job Description          
       Design and implement high intensity stress workloads using PyTorch and Triton Exercise core MAIA execution paths including compute memory DMA and collectives Enable early detection of performance cliffs stability issues and system bottlenecks across simulator and real hardware Improve platform maturity reduce latestage escapes and increase confidence for broader internal and external adoption Develop PyTorch workloads stressing modellevel execution such as large GEMMs attention patterns MoElike behavior mixed precision and longrunning loops Author custom Triton kernels to stress hardware execution units memory hierarchies and synchronization paths Build parameterized stress harnesses scalable by problem size number of devices and runtime duration Integrate workloads with existing profiling monitoring and failure triage tooling Collaborate with platform firmware and SDK teams to target known risk areas and emerging issues Document usage patterns and provide reproducible scripts for lab and continuous integration CI usage

Roles and Responsibilities :         Develop and maintain a library of reusable PyTorch stress workloads Create Tritonbased micro and macrokernels designed specifically for stress and saturation testing Build and support test harnesses and scripts for singledevice and multidevice execution Ensure workload designs align with platform risk areas and emerging hardwaresoftware issues Collaborate crossfunctionally with platform firmware and SDK teams to refine stress tests Provide comprehensive documentation describing workload intent configuration options and expected stress characteristics Support profiling monitoring and failure triage by integrating stress workloads with existing tools Deliver reproducible and scalable testing solutions for lab and CI environments

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