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Remote Cuda Developer Jobs in Freeport, PA (NOW HIRING)

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Remote Cuda Developer information

See Freeport, PA salary details

$79.6K

$97.7K

$129.1K

How much do remote cuda developer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for remote cuda developer in Freeport, PA is $97,689.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,800.00 and $109,600.00 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 cities near Freeport, PA are hiring for Remote Cuda Developer jobs? Cities near Freeport, PA with the most Remote Cuda Developer job openings:
Remote Cuda Developer jobs near you
Machine Learning Systems Engineer

Machine Learning Systems Engineer

Motional

Pittsburgh, PA • On-site, Remote

Other

Posted 16 days ago

Job description

Mission Summary:

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models.

What you'll be doing:

  • Performance Profiling & Optimization: Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.
  • Distributed Training: Optimize distributed training pipelines using frameworks such as PyTorch Distributed.
  • Kernel Development: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
  • Data Pipeline Engineering: Optimize robust data loading pipelines that maximize training throughput.

What we're looking for:

  • Education: Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.
  • Software Engineering: Strong proficiency in Python.
  • ML Frameworks: Extensive hands-on experience with PyTorch.
  • ML Knowledge: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.
  • Problem Solving: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges.

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

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