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Remote Gpu Programming Jobs in Georgia (NOW HIRING)

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... Profile GPU workloads using tools such as N sight Compute, Nsight Systems, nvprof, and ...

Machine Learning & Operations Engineer

Atlanta, GA · On-site +1

$66K - $90K/yr

This is a fully remote position, working cross-functionally with research and engineering teams ... Optimize GPU/compute utilization across cloud and on-prem environments. * Deploy, monitor, and ...

Machine Learning & Operations Engineer

Atlanta, GA · Remote

$66K - $90K/yr

This is a fully remote position, working cross-functionally with research and engineering teams ... Optimize GPU/compute utilization across cloud and on-prem environments. * Deploy, monitor, and ...

Machine Learning & Operations Engineer

Atlanta, GA · Remote

$66K - $90K/yr

This is a fully remote position, working cross-functionally with research and engineering teams ... Optimize GPU/compute utilization across cloud and on-prem environments. * Deploy, monitor, and ...

Data Center Engineer I

Atlanta, GA · Remote

$66K - $83K/yr

Submitting and tracking remote work requests and managing server and component inventory ... Strong troubleshooting skills in networking, hardware, GPU and Linux operating systems, with ...

... remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ... GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and ...

Remote Gpu Programming information

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, and why are they important?

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 job categories do people searching Remote Gpu Programming jobs in Georgia look for? The top searched job categories for Remote Gpu Programming jobs in Georgia are:
What cities in Georgia are hiring for Remote Gpu Programming jobs? Cities in Georgia with the most Remote Gpu Programming job openings:

CUDA Kernel Engineer

PRAGMATIKE

Atlanta, GA • Remote

Full-time

Medical, Dental, Vision, Retirement

Posted 7 days ago


Job description

Location: Remote US
Start date: ASAP
Languages: English (required)

About the Role

Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers.

We are searching for a CUDA Kernel Engineer who has hands-on experience developing and optimizing NVIDIA CUDA kernels from scratch. You will work on the GPU performance layer powering large-scale, high-throughput AI systems used by Fortune 500 customers.

This role is ideal for someone who deeply understands NVIDIA GPU architecture, memory hierarchy, warp-level execution, and profiling workflowsnot someone coming from generic hardware, FPGA, or non-NVIDIA compute backgrounds. You will directly influence the GPU efficiency, throughput, and scalability of mission-critical AI systems.

What Youll Do

  • Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency.
  • Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK.
  • Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead.
  • Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing.
  • Collaborate closely with AI systems, model acceleration, and backend distributed systems teams.
  • Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices.

What Were Looking For

  • Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries.
  • Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling).
  • Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it)
  • Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap).
  • Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling.

Bonus Points

  • Experience with multi-GPU or distributed GPU systems (NCCL, NVLink, MIG).
  • Background in GPU acceleration for ML frameworks or HPC workloads.
  • Knowledge of model inference optimization (TensorRT, CUDA Graphs, CUTLASS).
  • Exposure to compiler-level optimization or PTX/SASS analysis.
  • Startup experience or comfort working in fast-moving, ambiguous environments.

Why This Role Will Pivot Your Career

  • Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions.
  • Customer impact: Deploy AI solutions powering Fortune 500 clients.
  • Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave).
  • Funding & growth: Oversubscribed seed round, next funding in 2026.
  • Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale.
  • Culture & autonomy: Own critical systems while collaborating with world-class engineers.
  • Aspirational impact: Solve GPU/AI performance challenges few engineers ever face.

Benefits

  • Competitive salary & equity options
  • Sign-on bonus
  • Health, Dental, and Vision
  • 401k

Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.