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Remote Gpu Engineer Jobs in Boston, MA (NOW HIRING)

Motional

Perception Engineer

Boston, MA · On-site +1

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 ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · Remote

$125K - $165K/yr

Set up and manage the training environment, including GPU instances and required software. Train ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Set up and manage the training environment, including GPU instances and required software. Train ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Set up and manage the training environment, including GPU instances and required software. Train ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Motional

Perception Engineer

Boston, MA · On-site +1

$165K - $197K/yr

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 ...

Motional

Perception Engineer

Boston, MA · Remote

$165K - $197K/yr

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 ...

Motional

Machine Learning Systems Engineer

Boston, MA · On-site +1

$144K - $192K/yr

Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML ... be fully remote. The salary range for this role is an estimate based on a wide range of ...

Motional

Machine Learning Systems Engineer

Boston, MA · On-site +1

$144K - $192K/yr

Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML ... be fully remote. The salary range for this role is an estimate based on a wide range of ...

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Remote Gpu Engineer information

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How much do remote gpu engineer jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for remote gpu engineer in Boston, MA is $58.26, according to ZipRecruiter salary data. Most workers in this role earn between $47.02 and $67.64 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.

What are the most commonly searched types of Gpu Engineer jobs in Boston, MA?

The most popular types of Gpu Engineer jobs in Boston, MA are:

What are popular job titles related to Remote Gpu Engineer jobs in Boston, MA?

For Remote Gpu Engineer jobs in Boston, MA, the most frequently searched job titles are:

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The top searched job categories for Remote Gpu Engineer jobs in Boston, MA are:

Remote Gpu Engineer jobs near you

Staff Engineer, Inference Optimizations

DigitalOcean

Boston, MA • Remote

$191K - $239K/yr

Full-time

Posted 24 days ago

Job description

DigitalOcean is seeking a Senior Engineer 2 to play a key technical role in our AI Inference Optimization team. DigitalOcean aims to be the Inference Cloud of choice for digitally native companies and you will help ensure we can offer the industry-leading performance for our inference services. You will be responsible for the architectural decisions that maximize throughput and minimize latency for the world's most advanced large models. As an IC leader, you will act as a force multiplier for the engineering organization, solving the most complex bottlenecks in memory bandwidth and compute utilization while guiding the technical roadmap for our high-performance inference fleet.

What You'll Do:
  • Performance Architecture: Lead the technical strategy for benchmarking and performance optimizations at the inference engine and GPU kernel layers, ensuring our infrastructure extracts maximum value from every TFLOP.
  • Deep-Dive Optimization: Engineer solutions for complex performance issues, including attention layer optimizations, memory and precision management, and advanced parallelization across multi-node GPU clusters. 
  • Technological Innovation: Proactively implement cutting-edge optimization techniques to keep DigitalOcean at the forefront of the Gen AI landscape. Some examples of projects you may work on:
    • Improving batch size performance using AMD's AITER library for AMD MI355X - identify and tune AITER's CK (composable kernel) or ASK (assembly) to optimize FP8 / BF16 
    • Identify kernel fusion opportunities for GLM-5 kernels for different layers of the Transformer block (FlashAttention, RMS Norm)
    • Tune expert gateway router kernels for MoE models like Qwen3-235B, DeepSeek V3, GLM-5 etc
  • Hardware & Ecosystem Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton), advising on hardware procurement and software integration.
  • Precision Optimization: Develop and deploy state-of-the-art quantization techniques (FP8, INT8, and experimental FP4) to double throughput without losing accuracy.
  • Technical Mentorship: Lead by example through high-quality code and design reviews, elevating the technical bar for the team without the administrative overhead of direct management.
  • Strategic Collaboration: Partner with Product Management and TPMs to translate "theoretical hardware limits" into "shippable product features," ensuring our platform is both powerful and developer-friendly.
  • Community Leadership: Maintain a strong presence in the GPU infrastructure and model performance optimization communities, contributing to and integrating the best of open-source AI.
What You'll Bring to DigitalOcean:
  • Technical Depth: 5+ years of experience in high-performance computing or AI infrastructure, with a proven track record of solving compute utilization and memory bandwidth bottlenecks.
  • Gen AI Literacy: Deep familiarity with the Gen AI (LLM, VLM, LMM) landscape, including the specific quirks and architectural requirements of major model families.
  • Optimization Expert: Hands-on experience with attention-layer optimizations and parallelization strategies across distributed GPU environments.
  • Hardware Fluency: Comprehensive understanding of NVIDIA and AMD GPU architectures and their respective software ecosystems (CUDA, ROCm, etc.).
  • Open Source Mastery: Extensive experience integrating, building with, and contributing to open-source software projects.
  • Systems Design: Excellent system design skills, particularly related to low-level GPU programming - optimization, memory access patterns, and parallel execution.
  • Leadership through Influence: Experience acting as a technical lead, driving design and delivery through cross-functional alignment and expert-level delegation.
  • Low-Level Mastery: Deep understanding of GPU architectures (SMs, Warp scheduling, Tensor Cores).
  • The Toolkit: Expert-level Triton or CUDA. If you've contributed to the Triton compiler or wrote custom CUDA kernels for a major LLM, we want you.
Compensation Range: 
  • $191,200 - $239,000

*This is a remote role

JR: 2026-7625

#LI-Remote

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