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Remote Cuda Developer Jobs in Boston, MA (NOW HIRING)

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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 ...

All JobsCuda Developer JobsRemote Cuda Developer JobsRemote Cuda Developer Jobs in Boston, MA

Remote Cuda Developer information

See Boston, MA salary details

$90.7K

$111.4K

$147.2K

How much do remote cuda developer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for remote cuda developer in Boston, MA is $111,356.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,800.00 and $124,900.00 per year, depending on experience, location, and employer.

How does a Remote CUDA Developer typically collaborate with team members across different locations?

As a Remote CUDA Developer, you will frequently collaborate with cross-functional teams such as data scientists, software engineers, and product managers through virtual meetings, code reviews, and collaborative platforms like GitHub or GitLab. Clear communication and thorough documentation are essential since team members may be in different time zones. You can expect to participate in regular stand-ups, sprint planning, and peer programming sessions, ensuring alignment and smooth integration of your GPU-accelerated code into larger projects. Tools like Slack, Zoom, and project management platforms help maintain connectivity and workflow efficiency.

What is a Remote CUDA Developer?

A Remote CUDA Developer is a software engineer who specializes in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to develop parallel computing applications, often for high-performance tasks like machine learning, scientific computing, or data analysis. They work remotely, collaborating with teams online rather than being physically present in an office. These developers write and optimize code to run efficiently on NVIDIA GPUs, enabling applications to process large amounts of data much faster than traditional CPU-only solutions.

What are the key skills and qualifications needed to thrive as a Remote CUDA Developer, and why are they important?

To thrive as a Remote CUDA Developer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid understanding of GPU architecture, typically backed by a degree in computer science or a related field. Experience with NVIDIA CUDA toolkit, GPU debugging tools, and version control systems like Git is commonly required. Excellent problem-solving skills, self-motivation, and effective remote communication abilities help distinguish high performers in this role. These skills are vital for efficiently delivering high-performance computing solutions and collaborating seamlessly with distributed teams.

What is the difference between Remote Cuda Developer vs Remote Machine Learning Engineer?

AspectRemote Cuda DeveloperRemote Machine Learning Engineer
Required CredentialsCUDA programming certifications, computer science degreeMachine learning certifications, data science background
Work EnvironmentSoftware development, GPU optimizationModel development, data analysis
Industry UsageHigh-performance computing, gaming, AIAI, data science, predictive modeling

Remote Cuda Developers focus on GPU programming and optimization using CUDA, primarily in high-performance computing and AI applications. Remote Machine Learning Engineers develop and deploy machine learning models, often utilizing GPU resources but with a broader focus on data and algorithms. While both roles may involve GPU expertise, Cuda Developers specialize in low-level programming, whereas Machine Learning Engineers work on model development and deployment.

What are popular job titles related to Remote Cuda Developer jobs in Boston, MA? For Remote Cuda Developer jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Remote Cuda Developer jobs in Boston, MA look for? The top searched job categories for Remote Cuda Developer jobs in Boston, MA are:
Remote Cuda Developer jobs near you
Staff Engineer, Inference Optimizations

Staff Engineer, Inference Optimizations

DigitalOcean

Boston, MA • Remote

$191K - $239K/yr

Other

Posted 3 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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