CUDA Kernel Engineer (Remote US)

Pragmatike is hiring a CUDA Kernel Engineer (Remote US) for a remote role open to applicants in United States. Level rates it ; you can apply on Pragmatike.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
This is listed as a remote role.
The posting mentions Databricks, CUDA and Nsight Compute among its tools and skills.
Level classes it as a senior role.
Pragmatike has 51 other open roles on Level.

AI in this role

Design, implement, and optimize custom CUDA kernels to power high-throughput AI systems.

databrickscudansight-computensight-systemsnvproftensorrtcutlass

cuda-programminggpu-optimizationcperformance-engineeringdistributed-systems

Location: SF on-site
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.

How we rate this

CUDA Kernel Engineer (Remote US) at Pragmatike rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

CUDA Kernel Engineer (Remote US) is an open job at Pragmatike.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Cambridge and is marked remote.
The job was posted on April 22, 2026.
Seniority: senior.
Applications are handled by Pragmatike. Apply on Pragmatike

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Cuda ProgrammingGPU OptimizationCPerformance EngineeringDistributed SystemsDatabricksCUDANsight Compute

Questions you could be asked

Tell me about a project where cuda programming was part of your work. What did you do?
Tell me about a project where gpu optimization was part of your work. What did you do?
Tell me about a project where c was part of your work. What did you do?
Tell me about a project where performance engineering was part of your work. What did you do?
Tell me about a project where distributed systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Cuda Programming, GPU Optimization, C, Performance Engineering, and Distributed Systems. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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