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Parallel Learning Jobs in California (NOW HIRING)

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Parallel Learning information

See California salary details

$34.5K

$81.4K

$159.9K

How much do parallel learning jobs pay per year?

As of Jul 1, 2026, the average yearly pay for parallel learning in California is $81,427.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,900.00 and $106,600.00 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 California? For Parallel Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Parallel Learning jobs in California look for? The top searched job categories for Parallel Learning jobs in California are:
What cities in California are hiring for Parallel Learning jobs? Cities in California with the most Parallel Learning job openings:
Parallel Learning jobs near you
Infographic showing various Parallel Learning job openings in California as of June 2026, with employment types broken down into 1% As Needed, 93% Full Time, 4% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $81,427 per year, or $39.1 per hour.
Software Engineer, SystemML - AI Networking

Software Engineer, SystemML - AI Networking

Meta

Menlo Park, CA

$183K/yr

Full-time

Posted 17 days ago

Meta rating

7.5

Company rating: 7.5 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

130th of 202 rated software companies

Job description

In this role, you will be a member of the AI Networking Software team and part of the bigger DC networking organization. The team develops and owns the software stack around NCCL (NVIDIA Collective Communications Library), which enables multi-GPU and multi-node data communication through HPC-style collectives. NCCL has been integrated into PyTorch and is on the critical path of multi-GPU distributed training. In other words, nearly every distributed GPU-based ML workload in Meta Production goes through the SW stack the team owns.At the high level, the team aims to enable Meta-wide ML products and innovations to leverage our large-scale GPU training and inference fleet through an observable, reliable and high-performance distributed AI/GPU communication stack. Currently, one of the team’s focus is on building customized features, SW benchmarks, performance tuners and SW stacks around NCCL and PyTorch to improve the full-stack distributed ML reliability and performance (e.g. Large-Scale GenAI/LLM training) from the trainer down to the inter-GPU and network communication layer. And we are seeking for engineers to work on the space of GenAI/LLM scaling reliability and performance.
Software Engineer, SystemML - AI Networking Responsibilities:
  • Tech-leading the collective communication library development on Meta's large-scale GPU training infra with a focus on GenAI/LLM scaling

Minimum Qualifications:
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Proven C/C++ and Python programming skills
  • Proven track record of leading successful projects
  • Effective leadership and communication skills
  • Specialized experience in one or more of the following machine learning/deep learning domains: Distributed ML Training, GPU architecture, ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine Learning frameworks (e.g. PyTorch)

Preferred Qualifications:
  • Experience with NCCL and distributed GPU performance analysis on RoCE/Infiniband
  • PhD in Computer Science, Computer Engineering, or relevant technical field
  • Knowledge of GPU architectures and CUDA programming
  • Knowledge of ML, deep learning and LLM
  • Experience with both data parallel and model parallel training, such as Distributed Data Parallel, Fully Sharded Data Parallel (FSDP), Tensor Parallel, and Pipeline Parallel
  • Experience in HPC and parallel computing
  • Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow
  • Experience in AI framework and trainer development on accelerating large-scale distributed deep learning models

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$183,997/year to $257,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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