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Temporary Software Engineer Gpu Jobs (NOW HIRING)

Google

Senior Software Engineer, GPU Performance

Sunnyvale, CA · On-site

$143K - $189K/yr

Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.). About the job Google's software engineers develop the next-generation ...

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How much do temporary software engineer gpu jobs pay per year?

As of Jun 9, 2026, the average yearly pay for temporary software engineer gpu in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Temporary Software Engineer GPU, you need a solid background in computer science, experience with GPU programming (such as CUDA or OpenCL), and proficiency in languages like C++ or Python. Familiarity with GPU development environments, debugging tools, and version control systems, along with any relevant certifications, is highly valuable. Strong problem-solving abilities, adaptability, and effective teamwork skills help set candidates apart in this fast-evolving field. These skills enable efficient development, optimization, and integration of GPU-accelerated applications, which is crucial for meeting project deadlines and technical goals.

What does a Temporary Software Engineer GPU do?

A Temporary Software Engineer GPU is responsible for designing, developing, and optimizing software that interacts with graphics processing units (GPUs), typically for a specific project or short-term period. Their duties may include writing code to improve GPU performance, working on graphics or compute-intensive applications, and collaborating with hardware and software teams to ensure efficient GPU utilization. These roles are often contract-based and require strong programming skills in languages such as C++ or CUDA, as well as a solid understanding of GPU architectures.

What are the typical projects a Temporary Software Engineer GPU might work on, and how do they collaborate with permanent team members?

As a Temporary Software Engineer GPU, you can expect to be assigned to specific, time-bound projects such as optimizing graphics performance, supporting new hardware integration, or debugging GPU-related issues. You’ll often work closely with permanent engineers, participating in code reviews, daily stand-ups, and cross-functional meetings to ensure alignment and knowledge transfer. Collaboration is key, as you may need to quickly get up to speed with existing codebases and tools, and contribute solutions that fit seamlessly into larger, ongoing projects. The role offers a fast-paced environment where adaptability and strong communication skills are highly valued.
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Temporary Software Engineer Gpu jobs near you

Senior Systems Software Engineer, GPU Compute

Nebius

Remote

Full-time

Posted 5 hours ago

Job description

About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
The role
We're looking for a Senior Software Systems Engineer to join our team and play a key role in the development of our cutting-edge hyperscaler platform. The GPU & InfiniBand team is responsible for enhancing and optimizing the core components of our Cloud platform, with a specific focus on GPU computing, InfiniBand networks, and the KVM/QEMU stack. You'll work closely with hardware virtualization and device emulation technologies, ensuring high performance and security in multi-GPU, HPC environments. The role involves analyzing, troubleshooting, and improving infrastructure to support new hardware, fine-tuning system performance, and automating fault detection and resolution in a complex system.
In this position, you will be responsible for:
  • Tuning the performance of GPU clusters and InfiniBand networks to ensure optimal operation in HPC and GPU-based environments.
  • Analyzing and troubleshooting the root cause of issues related to GPUs and InfiniBand networks, and proposing corrective actions.
  • Integrating new hardware into the existing infrastructure, including support for new GPU hardware through software stacks like Kubernetes, QEMU, and KVM.
  • Enhancing automation systems for proactive monitoring, detecting, and resolving issues in GPU and InfiniBand environments.
  • Configuring and managing GPU devices and InfiniBand fabrics, ensuring efficient and reliable operation.

We expect you to have:
  • 5+ years of professional experience in system-level software development (focused on performance optimization, low-level programming).
  • 3+ years of hands-on experience with Linux systems (administration, troubleshooting, and performance tuning).
  • In-depth understanding of server architecture, including PCIe devices, NICs, Linux OS/Kernel, and high-performance computing (HPC) systems.
  • Strong proficiency in one or more performance-oriented programming languages (C/C++, Go, Python).

It would be a plus if you have:
  • Experience with GPU end-to-end testing in a cluster environment using InfiniBand networking.
  • Proven track record of analyzing and optimizing the performance of HPC workloads (e.g., simulations, data analysis, AI/ML workloads).
  • Familiarity with RDMA, RoCE, and InfiniBand protocols for high-performance communication.
  • Background in Software-Defined Networking (SDN) and experience with HPC cluster networking.
  • Understanding of QEMU/KVM virtualization and managing virtualized environments.
  • Experience with deep learning frameworks such as PyTorch and TensorFlow, and their integration with HPC systems.
  • Familiarity with collective communication libraries like MPI and NCCL for distributed computing.

We offer competitive salaries ranging from $170k-$300k + equity based on your experience.
We conduct coding interviews as part of the process.
Benefits & Perks:
  • Competitive compensation
  • Career growth and learning opportunities
  • Flexibility and work-life balance
  • Collaborative and innovative culture
  • Opportunity to work on impactful AI projects
  • International environment and talented teams

What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.

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