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Remote Gpu Engineer Jobs (NOW HIRING)

Nvidia

$135K - $181K/yr

We are seeking a Principal Systems Engineer to define the vision and roadmap for memory management ... Deep understanding of memory hierarchies (GPU HBM, host DRAM, SSD, and remote/object storage) and ...

Mantari

Software Engineer

Torrance, CA · On-site +1

... a single GPU to multi-region GPU clusters in the cloud * Automate data ingest and feature ... Experience with numerical weather prediction, remote-sensing data, or geospatial intelligence

Capgemini Engineering

$73K - $174K/yr

At Capgemini Engineering, the world leader in engineering services, we bring together a global team ... I/O subsystems, GPU, SSD, FPGA * DEOS Expertise * Cache partitioning techniques and Resource ...

STN Inc

Site Reliability Engineer

San Francisco, CA · Remote

$67.25 - $89.25/hr

Remote (US) Department: Cloud Platform Engineering / SRE/Reliability Position summary The Site Reliability Engineer (SRE) owns reliability, observability, and incident response for the GPU One ...

Vultr

Staff AI/ML Infrastructure Engineer

$145K - $160K/yr

... 500 stipend for remote office setup in first year + $400 each following year * Internet ... Design and maintain GPU and bare metal infrastructure in containerized and physical environments

vCluster Labs

AI Infrastructure Engineer

New York, NY · Remote

$150K - $200K/yr

As an AI Infrastructure Engineer, your role will include: * Lead Technical Deployments: Drive end ... and we have a remote-first work culture. We are the leading platform for operating GPU ...

Biohub

Staff HPC Engineer

San Francisco, CA · On-site +1

We own the design, operation, and reliability of hybrid GPU AI clusters that power frontier AI ... remote dev, containerization, MLOps workflows). What You'll Bring Essential * Bachelor's or ...

Motional

Perception Engineer

Boston, MA · Remote

N322 BOSTON - REMOTE U.S. ONLY FULL-TIME What You'll Do: * Connect/drive project-level impact ... PyTorch, * GPU training, * acceleration and inference optimization, * TensorRT/CUDA, * CI/CD ...

New

Weekday AI

Game Tools & Modding Engineer

Remote We are looking for a Game Tools & Modding Engineer with strong expertise in graphics ... Performance Optimization * Optimize rendering performance across CPU, GPU, memory, and ...

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How much do remote gpu engineer jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for remote gpu engineer in the United States is $53.63, according to ZipRecruiter salary data. Most workers in this role earn between $43.27 and $62.26 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote GPU engineer?

To thrive as a Remote GPU Engineer, you need strong expertise in GPU architectures, parallel programming (CUDA/OpenCL), and a solid background in computer science or engineering. Familiarity with tools like CUDA Toolkit, performance profilers, and version control systems, as well as experience with relevant certifications, is typically required. Excellent problem-solving abilities, communication skills, and the capacity to collaborate effectively in remote, distributed teams are standout soft skills. These competencies ensure efficient GPU solution development, effective troubleshooting, and seamless teamwork in a remote engineering environment.

What is a remote GPU engineer?

Remote GPU Engineers are specialized software or hardware engineers who work primarily with Graphics Processing Units (GPUs) from a remote location. They focus on designing, optimizing, and maintaining GPU-based systems for applications such as machine learning, high-performance computing, and graphics rendering. These professionals often collaborate with teams virtually, leveraging cloud-based GPU resources and remote access tools. Their work enables companies to efficiently utilize GPU technology without requiring engineers to be on-site.

What are some common challenges faced by remote GPU engineers when collaborating with distributed teams?

Remote GPU Engineers often work with global teams, which can present challenges such as coordinating across different time zones, ensuring consistent communication, and managing access to high-performance hardware remotely. To overcome these hurdles, it's important to leverage collaboration tools, maintain clear documentation, and establish regular check-ins. Additionally, using remote desktop solutions and cloud-based GPU environments can help facilitate smoother development and debugging processes.
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What cities are hiring for Remote Gpu Engineer jobs? Cities with the most Remote Gpu Engineer job openings:
What are the most commonly searched types of Gpu Engineer jobs? The most popular types of Gpu Engineer jobs are:
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Remote Gpu Engineer jobs near you
Infographic showing various Remote Gpu Engineer job openings in the United States as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $111,552 per year, or $53.6 per hour.

Senior Site Reliability Engineer

Andromeda Cluster, Inc

San Francisco, CA • On-site, Remote

$67.25 - $89.25/hr

Full-time

Re-posted 17 days ago

Job description

Senior Site Reliability Engineer
Location: Global Remote / San Francisco • Full-Time
About Andromeda
Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers.
We began with a single managed cluster - but it filled almost instantly. Since then, we've been quietly building the systems, network, and orchestration layer that makes the world's AI infrastructure more accessible.
Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it's needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth.
Our long-term vision is to build the liquidity layer for global AI compute - a marketplace that moves the infrastructure and workloads powering AGI not dissimilar to the flows of capital in the world's financial markets.
We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering.
The Role
This is not a generalist SRE role.
You will design, operate, and debug large-scale GPU infrastructure used for distributed training and inference, working directly with customers pushing the limits of modern AI systems.
We're looking for engineers who have personally run GPU clusters in production, understand the failure modes of distributed training, and can reason about performance from network fabric → kernel → framework.
What You'll Own
  • GPU Cluster Architecture: Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training. Make topology-aware scheduling, networking, and storage decisions that directly impact training throughput and cost efficiency.
  • Customer Technical Partnership: Serve as the primary technical point of contact for customers running large-scale training workloads. Onboard, troubleshoot, and optimize, often in real time.
  • Reliability & Performance Engineering: Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure (ECC errors, NVLink degradation, NCCL timeouts). Own capacity planning across heterogeneous GPU fleets optimized for training throughput.
  • Networking & Fabric Health: Ensure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) that underpin distributed training. Diagnose and resolve fabric-level issues that degrade collective operations.
  • Observability: Build deep visibility into GPU utilization, memory pressure, interconnect throughput, training job performance, and hardware health. Go well beyond standard infrastructure metrics.
  • Automation & Tooling: Build production-grade automation for cluster provisioning, GPU health checks, job scheduling, self-healing, and firmware/driver lifecycle management.
  • Incident Leadership: Lead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks. Drive blameless postmortems and systemic fixes.

What We're Looking For
  • GPU Systems Expertise: Deep, hands-on experience operating large-scale GPU clusters (NVIDIA A100/H100/B200 or equivalent). You understand GPU memory hierarchies, ECC behavior, thermal throttling, and hardware failure modes from direct experience not documentation.
  • High-Performance Networking: Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training. You can diagnose why an all-reduce is slow, identify a degraded link in a fat-tree topology, and reason about congestion control at scale.
  • Distributed Training & ML Frameworks: Working knowledge of how large training jobs actually run - NCCL, CUDA, PyTorch distributed, DeepSpeed, Megatron, FSDP, or similar. You don't need to write the models, but you need to understand what's happening at the systems level when a 1,000-GPU training run stalls.
  • Linux & Systems Internals: Expert-level Linux knowledge: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, performance profiling at the syscall and hardware level.
  • Kubernetes & Orchestration: Strong experience running Kubernetes in production with GPU workloads, including device plugins, topology-aware scheduling, multi-cluster federation, and custom operators. Experience with Slurm or other HPC schedulers is equally valued.
  • Automation & Software Engineering: Strong engineering skills in Python, Go, or Bash. You build production-grade tools and services, not just scripts. Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent).
  • Observability & Monitoring: Hands-on experience building monitoring and alerting for GPU infrastructure, not just Prometheus/Grafana basics, but GPU-specific telemetry (DCGM, nvidia-smi, fabric manager metrics) integrated into actionable dashboards.
  • Incident Management: Proven track record leading incident response for complex distributed systems where the failure could be in hardware, firmware, networking, drivers, orchestration, or application code and you need to narrow it down fast.

Strong Candidates May Have
  • Distributed Storage: Experience with high-performance parallel file systems (VAST, Weka, Lustre, GPFS) and the checkpoint I/O and data-loading bottlenecks that come with large training runs.
  • Training Optimization: Experience profiling and optimizing distributed training performance: identifying stragglers, tuning collective communication strategies, improving MFU (Model FLOPs Utilization), and reducing idle GPU time across large runs.
  • Cluster Buildout & Hardware: Experience involved in physical cluster design - rack layout, power/cooling constraints, network topology design, and hardware validation/burn-in at scale.
  • Team Leadership: Experience leading or mentoring a team of infrastructure engineers. We're growing and need people who raise the bar for everyone around them.

Why You'll Love It Here
This is a high-impact, senior builder's role. You'll have significant ownership and autonomy to shape how our systems run at a foundational level, working directly with customers and providers while architecting the infrastructure backbone for reliable, scalable AI compute. You'll influence technical direction and help define what world-class AI infrastructure operations look like.
Andromeda Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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