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From Home Cuda Jobs (NOW HIRING)

Fuse Energy

CUDA Engineer

We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum ... in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the ...

SECURE RPO LLC

Software Engineer

Lexington, MA · On-site

Virtual/Work from Home * Hybrid Other Information Relevant to the Job RequirementMust Have * AWS ... CUDA Processing) * Java Development * Object-Oriented Software Design and Development * Software ...

Mat3ra

☁ Cloud HPC Engineer ☁

Walnut Creek, CA · On-site

$130K - $200K/yr

OpenMP/MPI/CUDA (intermediate) * Docker/Singularity or similar (intermediate) Other * ability to ... Work From Home * Free Food & Snacks * Wellness Resources * Stock Option Plan Compensation * $130 ...

Mat3ra

Cloud HPC Engineer

Walnut Creek, CA · On-site +1

$130K - $200K/yr

OpenMP/MPI/CUDA (intermediate) * Docker/Singularity or similar (intermediate) Other * ability to ... Work From Home * Free Food & Snacks * Wellness Resources * Stock Option Plan Compensation * $130 ...

Mat3ra

☁ Cloud HPC Engineer ☁

Walnut Creek, CA · On-site

$130K - $200K/yr

OpenMP/MPI/CUDA (intermediate) * Docker/Singularity or similar (intermediate) Other * ability to ... Work From Home * Free Food & Snacks * Wellness Resources * Stock Option Plan Compensation * $130 ...

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How much do from home cuda jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for from home cuda in the United States is $16.11, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $16.35 per hour, depending on experience, location, and employer.

What is a from home CUDA job?

From Home CUDA jobs are remote positions that involve working with CUDA, a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). Professionals in these roles typically work on tasks such as developing, optimizing, and debugging software that leverages GPU acceleration for high-performance computing applications. These jobs can be found in fields like artificial intelligence, machine learning, scientific computing, and graphics processing. Working from home in a CUDA-focused role often requires a strong background in C/C++ programming, experience with GPU architectures, and the ability to collaborate with distributed teams online.

What skills and qualifications are needed to thrive as a CUDA developer?

To thrive as a CUDA Developer, you need a strong background in programming (especially C/C++), parallel computing concepts, and ideally a degree in computer science or a related field. Familiarity with NVIDIA CUDA Toolkit, GPU programming, and performance profiling tools is typically required. Critical thinking, problem-solving, and effective communication skills help in designing efficient algorithms and collaborating with team members. These abilities are crucial to optimize code for high-performance computing applications and deliver scalable solutions.

What are common challenges faced by remote CUDA developers, and how can they overcome them?

Remote CUDA developers often encounter challenges such as coordinating with team members across different time zones, ensuring access to high-performance hardware for GPU programming, and maintaining clear communication about code changes and project requirements. To overcome these issues, it's helpful to establish regular check-ins, use collaborative development tools like Git, and leverage cloud-based GPU resources when local hardware is insufficient. Staying proactive with communication and documentation also helps keep projects on track and ensures a smooth workflow.

What is the difference between From Home Cuda vs Data Entry Clerk?

AspectFrom Home CudaData Entry Clerk
Required CredentialsBasic computer skills, sometimes certifications in data managementHigh school diploma or equivalent, basic computer skills
Work EnvironmentRemote, home-basedOffice or remote, depending on employer
Industry UsageFrequent in tech, e-commerce, and digital servicesCommon across various industries like healthcare, finance, retail
Job FocusManaging digital data, software toolsInputting and updating data into systems

From Home Cuda and Data Entry Clerk roles both involve working with digital data, but From Home Cuda typically requires more specialized skills or certifications and is often found in tech-related industries. Data Entry Clerks focus on inputting data and may have fewer certification requirements. Both roles can be remote, but From Home Cuda may involve more complex data management tasks.

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From Home Cuda jobs near you
Infographic showing various From Home Cuda job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 78% In-person, 11% Hybrid, and 11% Remote job distribution, with an average salary of $33,500 per year, or $16.1 per hour.

CUDA Engineer

Fuse Energy

Remote

Full-time

Medical

Posted 5 days ago

Job description

Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.
We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.
We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers.
As data centres become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure at the intersection of energy and AI. We're looking for a CUDA Engineer to write and optimise the low-level GPU code that powers our inference workloads: designing custom CUDA kernels, tuning performance across memory bandwidth and compute bottlenecks, and squeezing maximum throughput out of every GPU in our fleet, working at the level of SMs, warps and memory hierarchies.
Responsibilities
  • Write and optimise custom CUDA kernels for core transformer inference operations
  • Profile kernels to identify and eliminate bottlenecks in occupancy, memory throughput and warp divergence
  • Apply kernel fusion to reduce memory round-trips and launch overhead across inference pipelines
  • Optimise memory access patterns and manage the memory hierarchy for maximum bandwidth utilisation
  • Implement quantisation-aware kernels and mixed-precision arithmetic to reduce latency and memory footprint
  • Build and tune caching mechanisms for efficient autoregressive decoding
  • Tune kernel launch configurations for target GPU architectures
  • Benchmark kernels against existing baselines and drive measurable throughput and latency improvements
  • Write tests for CUDA code to catch performance and correctness regressions
  • Maintain internal CUDA libraries and contribute to team coding standards and documentation

Requirements
  • 4+ years writing production CUDA code, with a track record of shipping performance-critical kernels
  • Deep understanding of GPU microarchitecture: warps, occupancy, register pressure and memory hierarchy
  • Strong CUDA C++ skills, including streams and asynchronous execution
  • Hands-on experience profiling to diagnose compute-bound vs memory-bound bottlenecks
  • Experience with kernel fusion, memory coalescing and avoiding warp divergence
  • Experience writing quantised and mixed-precision kernels
  • Solid grasp of parallel algorithm design and numerical precision tradeoffs
  • Bonus: transformer/attention-style kernels or autoregressive decoding; building high-performance GPU libraries from scratch; HPC or latency-critical performance engineering; multi-GPU or multi-node kernel-level optimisation; comfortable reading PTX/SASS to validate kernel efficiency

Benefits
  • Competitive salary and eligibility for equity
  • Biannual bonus scheme
  • Fully expensed tech to match your needs
  • Private health insurance
  • Breakfast and dinner allowance for office-based employees

As we hire globally, benefits vary by location.

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