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Kernels Jobs (NOW HIRING)

xAI

Member of Technical Staff - Inference

Palo Alto, CA

$180K - $440K/yr

You will own everything from distributed infrastructure (global KV cache, continuous batching, load balancing, auto-scaling) to deep low-level optimizations (GPU kernels, quantization, speculative ...

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$132K

$173.5K

How much do kernels jobs pay per year?

As of Jun 25, 2026, the average yearly pay for kernels in the United States is $171,593.00, according to ZipRecruiter salary data. Most workers in this role earn between $173,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced when working as a Kernel Developer, and how can new team members best overcome them?

Kernel Developers often face challenges such as debugging low-level code, understanding complex hardware interactions, and ensuring system stability after making changes. New team members can overcome these hurdles by thoroughly reviewing existing documentation, leveraging debugging tools like kdump or ftrace, and collaborating closely with experienced colleagues during code reviews. Regular participation in team meetings and open-source community discussions also helps in staying updated on best practices and recent developments in kernel engineering.

What are the key skills and qualifications needed to thrive as a Kernel Engineer, and why are they important?

To thrive as a Kernel Engineer, you need expertise in operating system concepts, C/C++ programming, and low-level hardware interactions, often supported by a degree in computer science or a related field. Familiarity with source control systems like Git, debugging tools such as GDB, and experience with Linux kernel development are typically required. Attention to detail, problem-solving abilities, and effective communication are essential soft skills for this role. These skills and qualities are crucial for building stable, high-performance kernels and collaborating efficiently within development teams.

What is the difference between Kernels vs Network Administrators?

AspectKernelsNetwork Administrators
Required CredentialsKnowledge of operating systems, programming, Linux/UnixNetworking certifications (e.g., CCNA), IT experience
Work EnvironmentSystem-level development, OS configurationNetwork setup, maintenance, troubleshooting
Industry UsageSoftware development, OS designIT services, corporate networks

While Kernels focus on developing and maintaining core operating system components, Network Administrators manage and troubleshoot network infrastructure. Both roles require technical expertise but differ in scope and daily tasks, with Kernels working at the system level and Network Administrators handling network connectivity and security.

What are kernels in computing?

In computing, a kernel is the core component of an operating system that manages system resources and allows software and hardware to communicate. It handles tasks such as memory management, process scheduling, and input/output operations. The kernel operates at a low level, ensuring that different programs and devices work together smoothly and securely. There are different types of kernels, including monolithic and microkernels, each with advantages and trade-offs.
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What cities are hiring for Kernels jobs? Cities with the most Kernels job openings:
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Kernels jobs near you
Infographic showing various Kernels job openings in the United States as of June 2026, with employment types broken down into 1% Internship, 98% Full Time, and 1% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $171,593 per year, or $82.5 per hour.
Staff Software Engineer - GenAI Performance and Kernel

Staff Software Engineer - GenAI Performance and Kernel

Databricks

San Francisco, CA

$164K/yr

Other

Posted 19 days ago

Job description

P-1285

About This Role

As a staff software engineer for GenAI Performance and Kernel, you will own the design, implementation, optimization, and correctness of the high-performance GPU kernels powering our GenAI inference stack. You will lead development of highly-tuned, low-level compute paths, manage trade-offs between hardware efficiency and generality, and mentor others in kernel-level performance engineering. You will work closely with ML researchers, systems engineers, and product teams to push the state-of-the-art in inference performance at scale.

What You Will Do
  • Lead the design, implementation, benchmarking, and maintenance of core compute kernels (e.g. attention, MLP, softmax, layernorm, memory management) optimized for various hardware backends (GPU, accelerators)
  • Drive the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc.
  • Integrate kernel optimizations with higher-level ML systems
  • Build and maintain profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps
  • Lead performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation
  • Establish coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability
  • Influence system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries)
  • Mentor and guide other engineers working on lower-level performance, provide code reviews, help set best practices
  • Collaborate with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitor their impact
What We Look For
  • BS/MS/PhD in Computer Science, or a related field
  • Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads
  • Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc.
  • Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning
  • Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels
  • Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation)
  • Experience reasoning about numerical stability, mixed precision, quantization, and error propagation
  • Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems
  • Experience building high-performance products leveraging GPU acceleration
  • Excellent communication and leadership skills - able to drive design discussions, mentor colleagues, and make trade-offs visible
  • A track record of shipping performance-critical, high-quality production software
  • Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques

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