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

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 ...

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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 · 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 ...

Cornelis Networks

Senior ASIC Design Engineer

Boston, MA · On-site +1

... GPU, CPU and accelerator-based compute clusters at any scale. Our solutions drive breakthroughs in ... This is a remote position for employees residing within the United States. We offer a competitive ...

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 ...

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Remote Gpu Programming information

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$35.9K

$70.6K

$103.8K

How much do remote gpu programming jobs pay per year?

As of Aug 7, 2026, the average yearly pay for remote gpu programming in Boston, MA is $70,588.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,900.00 and $86,900.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals in remote GPU programming roles, and how can they be addressed?

Remote GPU programming roles often involve unique challenges such as managing high-latency connections to remote servers, troubleshooting hardware-specific issues without physical access, and ensuring code compatibility across different GPU architectures. Effective communication with distributed teams is crucial, as is using robust remote debugging tools and version control systems. Staying proactive with documentation and regularly syncing with team members can help address these obstacles and support successful project delivery.

What is remote GPU programming?

Remote GPU programming refers to the practice of developing and running code that utilizes graphics processing units (GPUs) on computers or servers that are accessed over a network, rather than on your local machine. This approach allows developers to leverage powerful, often cloud-based, GPU resources to handle computationally intensive tasks like machine learning, scientific simulations, or rendering without needing specialized hardware themselves. It often involves using remote desktop tools, cloud platforms, or custom APIs to access and manage GPU resources remotely.

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

To thrive as a Remote GPU Programmer, you need in-depth knowledge of parallel computing, proficiency in programming languages like C/C++, and experience with GPU architectures, often backed by a degree in computer science or a related field. Familiarity with technical tools such as CUDA, OpenCL, and GPU profiling/debugging systems is commonly required, along with certifications in GPU programming or high-performance computing. Strong problem-solving abilities, self-motivation, and effective remote communication skills help individuals excel in distributed teams. These competencies are crucial for efficiently developing and optimizing GPU-accelerated applications while collaborating across remote environments.
What are the most commonly searched types of Gpu Programming jobs in Boston, MA? The most popular types of Gpu Programming jobs in Boston, MA are:
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Remote Gpu Programming jobs near you

Staff Engineer, Inference Optimizations

DigitalOcean

Boston, MA • Remote

$191K - $239K/yr

Full-time

Posted 15 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

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