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Google Hpc Jobs (NOW HIRING)

Metrum Research Group

Associate Product Owner

Boston, MA · On-site

$35 - $40/hr

Job Summary The Associate Product Owner helps shape the future of our AI- and HPC-powered platform ... Fluency with Google Suite or MS Office. Proficiency using Mac OS preferred. Physical Demands The ...

Metrum Research Group

Associate Product Owner

Boston, MA · On-site +1

$35 - $40/hr

Job Summary The Associate Product Owner helps shape the future of our AI- and HPC-powered platform ... Fluency with Google Suite or MS Office. Proficiency using Mac OS preferred. Physical Demands The ...

Metrum Research Group

Associate Product Owner

Job Summary The Associate Product Owner helps shape the future of our AI- and HPC-powered platform ... Fluency with Google Suite or MS Office. Proficiency using Mac OS preferred. Physical Demands The ...

TylSemi

Mixed Signal RTL Design

San Jose, CA · On-site

$175K - $350K/yr

... HPC power delivery. * Knowledge of advanced topologies: 3-level buck, multi-level converters ... Google, Microsoft, Amazon, Meta, OpenAI - they're all designing their own ASICs. The $50B custom ...

TylSemi

SoC Architect

San Jose, CA · On-site

$175K - $350K/yr

Proven track record of architecting complex SoCs for AI, HPC, or Networking * Deep understanding of ... Google, Microsoft, Amazon, Meta, OpenAI - they're all designing their own ASICs. The $50B custom ...

Rivian

Cloud Engineer - Cloud Operations

Atlanta, GA · On-site

$97K - $122K/yr

... HPC clusters, and large-scale storage. * Implement and improve observability best practices (e.g. AWS CloudWatch, Google Cloud Platform Monitoring) and participate in on-call rotations and incident ...

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How much do google hpc jobs pay per hour?

As of Jun 26, 2026, the average hourly pay for google hpc in the United States is $27.93, according to ZipRecruiter salary data. Most workers in this role earn between $20.67 and $33.65 per hour, depending on experience, location, and employer.

What is Google HPC?

Google HPC (High Performance Computing) refers to Google's infrastructure and services that support large-scale computational tasks, often involving specialized hardware, parallel processing, and cloud-based resources. Jobs in this field typically require knowledge of distributed systems, programming skills, and familiarity with tools like Kubernetes or TensorFlow.

What is L1, L2, L3, and L4 in Google?

In the context of a Google HPC (High Performance Computing) environment, L1, L2, L3, and L4 typically refer to different levels of cache memory hierarchy or job priority levels. L1 cache is the fastest and closest to the processor cores, while L2 and L3 are larger but slightly slower caches. L4, when used, often denotes an even larger cache or memory level, or a specific tier in resource allocation, depending on the system architecture. Understanding these levels helps optimize computational performance and resource management in high-performance jobs.

What is a Google HPC engineer?

A Google HPC (High Performance Computing) engineer is a professional who designs, implements, and manages high-performance computing solutions using Google Cloud technologies. Their responsibilities include optimizing cloud infrastructure for computationally intensive workloads, supporting scientific and engineering applications, and ensuring efficient use of resources. These engineers work with clusters, parallel computing, and specialized hardware to enable large-scale data processing and advanced research. They collaborate closely with research teams, software developers, and IT specialists to deliver scalable and reliable HPC solutions.

What are the key skills and qualifications needed to thrive as a Google HPC (High Performance Computing) Engineer, and why are they important?

To thrive as a Google HPC Engineer, you need a strong background in computer science, parallel computing, and large-scale system architecture, often supported by a relevant degree and experience with HPC workloads. Familiarity with cloud platforms (like Google Cloud Platform), job schedulers (Slurm, PBS), and programming languages (Python, C/C++, MPI) is typically required, along with relevant certifications. Excellent problem-solving, collaboration, and communication skills help you work efficiently with cross-functional teams and clients. These skills and qualities are vital to optimize performance, ensure system reliability, and deliver scalable computing solutions for complex workloads.

What are the typical collaboration opportunities for someone working in a Google HPC (High Performance Computing) role?

Professionals in a Google HPC role frequently collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to design and optimize scalable computing solutions. You’ll often work closely with researchers to understand their computational needs and with cloud infrastructure teams to ensure efficient resource allocation. This collaborative environment helps you stay at the forefront of technological advances, while also allowing you to develop expertise in both technical and interpersonal skills. Regular interactions with other teams foster innovation and provide ample learning opportunities.

Which job is highly paid in Google?

In Google, senior roles such as Staff Software Engineer, Engineering Manager, and Technical Lead tend to be the highest paid positions, often earning six-figure salaries plus bonuses and stock options. These roles require extensive experience, advanced technical skills, and leadership capabilities. Compensation varies based on location, experience, and performance.

What is the future of HPC?

The future of high-performance computing (HPC) involves increasing computational power through advancements in hardware such as GPUs and specialized processors, as well as software optimization for parallel processing. HPC professionals will need skills in programming, system architecture, and data management to support applications in scientific research, AI, and big data analytics. As demand grows, roles in HPC are expected to expand across industries requiring large-scale data processing and simulation capabilities.
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Google Hpc jobs near you
Infographic showing various Google Hpc job openings in the United States as of June 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 33% In-person, and 67% Remote job distribution, with an average salary of $58,088 per year, or $27.9 per hour.

Senior ML Infrastructure Engineer (PyTorch, Kubernetes, GPU Training)

Finoit Inc.

Redwood City, CA • On-site

$132K - $180K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Senior ML Infrastructure Engineer (PyTorch, Kubernetes, GPU Training)

Short Job Description

We are seeking a Senior ML Infrastructure Engineer to design and scale the infrastructure powering large-scale machine learning training workloads. In this role, you'll build high-performance GPU training platforms, optimize distributed training pipelines, and improve the developer experience for ML researchers.

Responsibilities:

  • Design and scale distributed ML training infrastructure for large GPU clusters.
  • Build and optimize training pipelines using PyTorch, DeepSpeed, and distributed training frameworks.
  • Develop and maintain job scheduling systems using Kubernetes and/or SLURM.
  • Create high-throughput data pipelines for large-scale multimodal datasets.
  • Optimize GPU utilization, memory efficiency, and overall system performance.
  • Build low-latency inference pipelines for production ML deployments.

Required Skills:

  • 7+ years of experience in ML Infrastructure, HPC, or Distributed Systems.
  • Strong experience with PyTorch, DeepSpeed, FSDP, ZeRO, or similar distributed training frameworks.
  • Hands-on experience with Kubernetes, cloud platforms (AWS/Google Cloud Platform), and containerized environments.
  • Strong understanding of distributed systems, GPU optimization, NCCL, memory management, and performance tuning.
  • Experience building scalable ML infrastructure from development through production.

Location: Redwood City, CA (On-site)
Employment Type: Full-Time

Nice to Have:

  • Experience with multimodal AI, robotics data pipelines, Triton, TensorRT, custom ML kernels, or ML compiler/runtime optimization.

Apply