ZipRecruiter

Log In

1

Parallel Learning Jobs in California (NOW HIRING)

Apple

Machine Learning Compiler Engineer

Sunnyvale, CA

$126.80K - $190.90K/yr

... parallel, or heterogeneous execution environments, with a solid understanding of shared memory ... learning frameworks and tools Familiarity with Just-in-Time (JIT) compilation and dynamic ...

next page

Showing results 1-20

People also search for

All JobsParallel Learning JobsParallel Learning Jobs in California

Parallel Learning information

See California salary details

$34.5K

$81.4K

$159.9K

How much do parallel learning jobs pay per year?

As of Jun 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 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.

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 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.

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 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 May 2026, with employment types broken down into 52% Full Time, 40% Part Time, and 8% Nights. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $81,427 per year, or $39.1 per hour.
(Senior) Software Engineer, Deep Learning

(Senior) Software Engineer, Deep Learning

pony.ai

Fremont, CA • On-site

$140K - $280K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 19 days ago

Job description

Founded in 2016 in Silicon Valley, Pony.ai has quickly become a global leader in autonomous mobility and is a pioneer in extending autonomous mobility technologies and services at a rapidly expanding footprint of sites around the world. Operating Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units, Pony.ai is an industry leader in the commercialization of autonomous driving and is committed to developing the safest autonomous driving capabilities on a global scale. Pony.ai’s leading position has been recognized, with CNBC ranking Pony.ai #10 on its CNBC Disruptor list of the 50 most innovative and disruptive tech companies of 2022. In June 2023, Pony.ai was recognized on the XPRIZE and Bessemer Venture Partners inaugural “XB100” 2023 list of the world’s top 100 private deep tech companies, ranking #12 globally. As of August 2023, Pony.ai has accumulated nearly 21 million miles of autonomous driving globally. Pony.ai went public at NASDAQ in November 2024.

Responsibility
  • Work with experts in the field of self-driving vehicles on software architecture and design, system and module design, evaluation metrics, specification and implementation of test and regression frameworks.
  • Design and develop large-scale foundation models trained on vast of real world data
  • Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines; and streamline them to run in real-time on the cars.
  • Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs)
  • Optimize deep learning models to run robustly under tight run-time constraints.

Requirements

  • Master in Computer Science, or at least 2 years of equivalent industry experience in similar technical fields.
  • Solid understanding of data structures, algorithms, parallel computing, code optimization and large scale data processing.
  • Experience in applied machine learning including data collection and analysis, evaluation and feature engineering.
  • Expertise in C++/Python.
  • Strong communication skills and team spirit.
Preferred Experience
  • PhD in Deep Learning, Machine Learning, Robotics, Natural Language Processing, or similar technical field of study.
  • Publications on top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/ICLR/AAAI/IJCV/PAMI
  • Experience in applying ML/DL for behavior prediction, imitation learning, motion planning.
  • Experience in deploying deep learning algorithms for real time applications, with limited computing resources.
  • Experience in convex optimization, computational geometry or linear algebra.
  • Experience in GPU/CUDA/TensorRT
Compensation and Benefits

Base Salary Range: $140,000 - $280,000 Annually

Compensation may vary outside of this range depending on many factors, including the candidate’s qualifications, skills, competencies, experience, and location. Base pay is one part of the Total Compensation and this role may be eligible for bonuses/incentives and restricted stock units.

Also, we provide the following benefits to the eligible employees:

  • Health Care Plan (Medical, Dental & Vision)
  • Retirement Plan (Traditional and Roth 401k)
  • Life Insurance (Basic, Voluntary & AD&D)
  • Paid Time Off (Vacation & Public Holidays)
  • Family Leave (Maternity, Paternity)
  • Short Term & Long Term Disability
  • Free Food & Snacks