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Parallel Learning Jobs in Seattle, WA (NOW HIRING)

NVIDIA

Senior Software Engineer, CUTLASS Platform

Redmond, WA · On-site

$137K - $180K/yr

... parallel processing architectures. • Hands-on compiler design experience, particularly in MLIR. • Understanding of deep learning models, algorithms, and frameworks. Company : NVIDIA is a ...

Georgia IT Inc

Azure Data Engineer

Redmond, WA · On-site

$128K - $154K/yr

Understanding of big data concepts and architectures, such as distributed computing, parallel processing, streaming analytics, and machine learning. * Familiarity with DevOps practices and tools for ...

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All JobsParallel Learning JobsParallel Learning Jobs in Seattle, WA

Parallel Learning information

See Seattle, WA salary details

$39.8K

$93.9K

$184.4K

How much do parallel learning jobs pay per year?

As of Jul 1, 2026, the average yearly pay for parallel learning in Seattle, WA is $93,895.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,900.00 and $122,900.00 per year, depending on experience, location, and employer.

What is the difference between Parallel Learning vs Data Analysis?

AspectParallel LearningData Analysis
Required CredentialsOften requires knowledge of machine learning, programming, and statisticsTypically requires statistics, Excel, and data visualization skills
Work EnvironmentTech-focused, research, and development settingsBusiness, finance, healthcare, and various industries
Employer & Industry UsageTech companies, startups, research institutionsCorporations, consulting firms, government agencies
Common Search & Comparison IntentUnderstanding roles related to machine learning and AIAnalyzing data to inform business decisions

Parallel Learning involves developing machine learning models and algorithms, often in tech or research environments, requiring programming and statistical skills. Data Analysis focuses on examining datasets to extract insights, used across many industries like finance and healthcare. While both roles involve working with data, Parallel Learning emphasizes creating models, whereas Data Analysis emphasizes interpreting data for decision-making.

What is parallel learning?

Parallel learning is an educational approach where students receive supplemental instruction or interventions alongside their regular classroom learning. This method is often used to provide personalized support, such as special education services or targeted skill development, without removing students from their standard curriculum. By running interventions 'in parallel' with general education, students can address specific learning needs while staying engaged with their peers. Parallel learning can take many forms, including small group sessions, individualized instruction, or online modules.

How does a professional in Parallel Learning typically collaborate with educators, families, and specialists to support student success?

Professionals in Parallel Learning, such as educational therapists or learning specialists, play a key role in fostering collaboration between students, educators, families, and other specialists. They often coordinate with teachers to adapt curriculum, communicate with families about progress and strategies, and consult with speech-language pathologists or occupational therapists as needed. This interdisciplinary teamwork ensures that interventions are aligned and that each student receives consistent, individualized support. Regular meetings, progress updates, and shared goal-setting are common practices in this collaborative environment.

What are the key skills and qualifications needed to thrive as a Learning Specialist at Parallel Learning, and why are they important?

To thrive as a Learning Specialist at Parallel Learning, you generally need a background in education, special education, or psychology, often with relevant state certification or licensure. Familiarity with digital assessment tools, remote learning platforms, and individualized education program (IEP) software is typically required. Exceptional interpersonal skills, patience, and adaptability distinguish top performers in supporting diverse learners and collaborating with families and teams. These skills ensure personalized, effective interventions and help students reach their educational goals in a virtual environment.
What are popular job titles related to Parallel Learning jobs in Seattle, WA? For Parallel Learning jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Parallel Learning jobs in Seattle, WA look for? The top searched job categories for Parallel Learning jobs in Seattle, WA are:
Parallel Learning jobs near you
Infographic showing various Parallel Learning job openings in Seattle, WA as of June 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $93,895 per year, or $45.1 per hour.
Senior Deep Learning Compiler Engineer - XLA

Senior Deep Learning Compiler Engineer - XLA

Nvidia

Redmond, WA

$117K - $160K/yr

Full-time

Posted 5 days ago

Job description

NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as "the AI computing company".

We are looking for versatile software engineers for our XLA team. NVIDIA is at the center for the AI revolution that's transforming how people live, work, and interact with technology. Come join us to build high-performance, production-grade software that's at the core of next-generation AI systems.

What you will be doing:

In this role, develop compiler optimization algorithms for deep learning workloads. You will optimize inference and training performance for the JAX framework and the OpenXLA compiler on NVIDIA GPUs at scale. You'll collaborate with our partners in deep learning framework teams and our hardware architecture teams to accelerate the next generation of deep learning software. The scope of these efforts include:

  • Crafting and implementing compiler optimization techniques for deep learning network graphs.

  • Designing novel graph partitioning and tensor sharding techniques for distributed training and inference.

  • Performance tuning and analysis.

  • Code-generation for NVIDIA GPU backends using open-source compilers such as MLIR, LLVM and OpenAI Triton.

  • Designing user facing features in JAX and related libraries and other general software engineering work.

  • Working closely with GPU hardware engineering teams to design AI compiler software features for next-generation GPUs.

What we need to see:

  • Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field (or equivalent experience).

  • 4+ years of relevant work or research experience in performance analysis and compiler optimizations.

  • Ability to work independently, define project goals and scope, and lead your own development effort adopting clean software engineering and testing practices.

  • Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.

  • Strong foundation in architecture of CPU, GPUs or other high performance hardware accelerators. Knowledge of high-performance computing and distributed programming.

  • CUDA or OpenCL programming experience is desired but not required.

  • Experience with the following technologies is a huge plus: XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and deep learning framework design.

  • Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team. A history of mentoring junior engineers and interns is a bonus.

Ways to stand out from the crowd:

  • Experience working deep learning frameworks such as JAX, PyTorch or TensorFlow.

  • Extensive experience with CUDA or with GPUs in general.

  • Experience with open-source compilers such as XLA, LLVM, MLIR or TVM.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 1, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearning
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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