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High Performance Driving Jobs in California (NOW HIRING)

Transdev

Manual Driving Instructor

San Francisco, CA

$33 - $38/hr

High School Diploma or GED required * Previous defensive driving instructor experience; i.e. driving school, DMV road testing, and or high-performance driving background preferred * Minimum 6 months ...

Transdev

Manual Driving Instructor

San Francisco, CA · On-site

$33 - $38/hr

High School Diploma or GED required * Previous defensive driving instructor experience; i.e. driving school, DMV road testing, and or high-performance driving background preferred * Minimum 6 months ...

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All JobsHigh Performance Driving JobsHigh Performance Driving Jobs in California

High Performance Driving information

See California salary details

$10.9K

$73.6K

$80.4K

How much do high performance driving jobs pay per year?

As of Jul 2, 2026, the average yearly pay for high performance driving in California is $73,613.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,400.00 and $79,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a High Performance Driving Instructor, and why are they important?

To thrive as a High Performance Driving Instructor, you need advanced driving skills, thorough knowledge of vehicle dynamics, and experience in motorsports or performance driving events, often supported by relevant certifications from organizations like SCCA or NASA. Familiarity with data logging systems, in-car communication tools, and track safety protocols is essential. Strong communication, patience, and situational awareness enable instructors to effectively teach and ensure student safety on the track. These skills are critical for delivering high-quality instruction, maximizing learning, and maintaining safety in high-speed environments.

What is high performance driving?

High performance driving refers to the skillful operation of a vehicle at or near its limits of speed, handling, and braking, typically in a controlled environment such as a racetrack. It involves understanding vehicle dynamics, advanced driving techniques, and safety protocols to maximize both speed and control. Many enthusiasts participate in high performance driving events (HPDEs) to improve their skills, often under the supervision of instructors. Unlike racing, the focus is on driver improvement rather than competition, making it accessible to a wide range of skill levels. Safety is a top priority, with strict rules and guidelines to ensure a secure learning experience.

What are some common challenges faced by High Performance Driving Instructors, and how can they be overcome?

High Performance Driving Instructors often encounter challenges such as adapting teaching methods for students with varying skill levels, maintaining safety in a fast-paced environment, and effectively communicating technical feedback under pressure. Overcoming these challenges requires strong observation skills, patience, and the ability to tailor instruction to individual needs. Regularly updating technical knowledge and participating in instructor development programs can also help instructors stay current with best practices and safety protocols.

What is the difference between High Performance Driving vs Race Car Driver?

AspectHigh Performance DrivingRace Car Driver
Required CredentialsDriving certifications, safety trainingDriving licenses, racing licenses, specialized training
Work EnvironmentTrack days, driver training, automotive eventsRace tracks, competitions, professional racing circuits
Employer & IndustryDriving schools, automotive events, corporate trainingRacing teams, motorsport organizations, sponsors

High Performance Driving focuses on advanced driving skills for safety and vehicle control, often in training or recreational settings. Race Car Drivers compete professionally in races, requiring specialized racing licenses and intense training. While both roles involve high-speed driving, High Performance Driving emphasizes skill development and safety, whereas Race Car Drivers compete in high-stakes races.

What are popular job titles related to High Performance Driving jobs in California? For High Performance Driving jobs in California, the most frequently searched job titles are:
What job categories do people searching High Performance Driving jobs in California look for? The top searched job categories for High Performance Driving jobs in California are:
What cities in California are hiring for High Performance Driving jobs? Cities in California with the most High Performance Driving job openings:
High Performance Driving jobs near you
Infographic showing various High Performance Driving job openings in California as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $73,613 per year, or $35.4 per hour.

Software Engineer, High Performance Computing

Eventual Computing

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 11 days ago

Job description

About Eventual
Every breakthrough Physical AI system - humanoid robots, autonomous vehicles, video generation models - is trained on petabytes of video, lidar, radar, and sensor data. But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not the multimodal corpora that power AI. Robotics and video-AI teams now lose 20-40% of their training time to dataloading alone. GPU bandwidth has grown 2-3× per generation. Storage and pipelines haven't. The gap widens every year.
Eventual was founded in 2022 to close it. Our open-source engine, Daft, is the distributed data engine purpose-built for multimodal AI - already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens. We are building a video-native index on top of our engine for Physical AI that streams curated datasets to GPUs at line rate. Saturates B200s today. Aimed at NVL72 and Vera Rubin tomorrow.
We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today. We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc, and angels from the co-founders of Databricks and Perplexity. We've assembled a world-class team from AWS, Render, Pinecone and Tesla. We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next.
Join our small (but powerful!) team working together 4 days/week in our SF Mission district office.
Your Role
As a Systems Engineer on the Dataloading team, you'll build the layer that turns multi-petabyte video corpora into dict[str, Tensor] already on the GPU at line rate. We work with the top labs training Physical AI on the newest generation hardware - H100, B200, GB200, NVL72, with Vera Rubin on the horizon - on billions of dollars worth of compute, in collaboration with partners that are the largest public AI companies on Earth. Our job is to keep those GPUs fed: rank-aware sampling, NVMe caching, video and sensor co-loading, random access into clips, decode pipelining. Streaming alone can already saturate a B200; the hard part is enabling the complex sampling patterns researchers actually need without giving up a single percentage point of MFU.
This is a systems engineering role for someone who feels physical pain when a system is slow. You won't need GPU experience on day one - we'll uplevel you on NVL72, CUDA, and SLURM. We will need you to bring real expertise on what happens between NVMe, network, memory, and CPU, and a deep instinct for where bytes go.
Key Responsibilities
  • Design and build the video-native dataloader: rank-aware, NVMe-cached, random-access into clips, returns tensors directly to the GPU.
  • Profile and optimize the full data path from object store → NVMe → page cache → host RAM → device RAM. Eliminate every avoidable copy and stall.
  • Saturate the latest hardware (B200, GB200, NVL72) on real customer training jobs. Push toward Vera Rubin bandwidth requirements.
  • Own performance benchmarks against customer baselines (custom DataLoaders, DALI, decord, LeRobot) and against our own historical numbers - regressions get caught at PR time.
  • Partner with researchers at our partner labs to land the loader in their training stack and measure MFU end-to-end.
  • Work cross-team with Storage Infrastructure on the index/format boundary and with Visual Understanding on the model-output ingestion path.

What we look for
  • Obsession with systems-level performance. You can recite Jeff Dean's "numbers every programmer should know" in your sleep. You eat flamegraphs for breakfast.
  • Strong opinions on io_uring - love it or hate it, you've earned the opinion.
  • Live and breathe Rust, C++, or C. You reach for them when it matters and you know why.
  • Strong familiarity with operating systems - page cache, scheduling, syscalls, NUMA, memory hierarchies.
  • A sense for where bytes actually go: NVMe vs. memory vs. network vs. PCIe vs. NVLink, and the throughput and latency budgets of each.

Nice to have
  • Experience working with GPUs is a plus, but you don't need it on day one.
  • Experience working with SLURM, Kubernetes for GPU workloads, or other HPC schedulers.
  • Hands-on CUDA experience.
  • Deep expertise on memory and caching subsystems - page cache tuning, hugepages, NUMA pinning, GPU-Direct Storage.
  • Worked on video decode pipelines (PyAV, decord, NVDEC) or PyTorch DataLoader internals.
  • Contributed to open-source systems projects in Rust/C++.

Perks & Benefits
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Competitive comp and meaningful startup equity.
  • Catered lunches and dinners for SF employees.
  • Commuter benefit.
  • Team-building events and poker nights.
  • Health, vision, and dental coverage.
  • Flexible PTO.
  • Latest Apple equipment.
  • 401(k) plan with match.

If slow systems evoke emotional pain for you and you want to spend the next few years making the most expensive GPU clusters on the planet earn their keep, we'd love to talk.

Apply