ZipRecruiter

Log In

1

From Home Gpu Jobs (NOW HIRING)

Tower Research Capital

GPU Systems Engineer

New York, NY · On-site

$200K - $300K/yr

Tower is home to some of the world's best systematic trading and engineering talent. We empower ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...

Tower Research Capital

GPU Systems Engineer

New York, NY · Hybrid

$200K - $300K/yr

Tower is home to some of the world's best systematic trading and engineering talent. We empower ... Design, deploy, and scale distributed GPU clusters, from hardware selection and network topology ...

previous pagenext page

Showing results 21-40

All JobsGpu JobsFrom Home Gpu Jobs

From Home Gpu information

See salary details

$13

$25

$51

How much do from home gpu jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for from home gpu in the United States is $25.61, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $29.33 per hour, depending on experience, location, and employer.

What is a from home GPU job?

'From Home GPU' jobs refer to remote work positions that require access to a powerful Graphics Processing Unit (GPU). These jobs typically involve tasks such as video rendering, machine learning, 3D modeling, or scientific computing, which need significant computational power provided by a GPU. Working from home in these roles allows professionals to use their own GPU-equipped hardware or connect to cloud-based GPU resources to complete their work. Common job titles include remote AI engineer, 3D artist, or video editor. The demand for such jobs has grown as more companies embrace remote work and cloud computing technologies.

What are the key skills and qualifications needed to thrive as a GPU programmer working from home?

To succeed as a remote GPU Programmer, you need strong proficiency in parallel computing, programming languages like C++ and Python, and a solid understanding of computer graphics or machine learning algorithms, often backed by a relevant degree or certifications. Familiarity with tools and frameworks such as CUDA, OpenCL, TensorFlow, and remote collaboration platforms is typically required. Excellent problem-solving abilities, self-motivation, and effective communication skills are crucial for thriving in a remote environment. These skills enable high-quality, efficient development and seamless teamwork, ensuring project success even when working from a distance.

What are common challenges faced by remote GPU computing professionals, and how can they be addressed?

Remote GPU computing professionals often encounter challenges such as ensuring reliable access to high-performance hardware, managing data security, and troubleshooting technical issues without on-site support. To address these, it's important to work with reputable cloud GPU providers, maintain secure data transfer protocols, and develop strong remote collaboration skills. Regular communication with IT support and staying updated on remote GPU tools can also help mitigate these challenges, ensuring smooth workflow and project delivery.

What is the difference between From Home Gpu vs From Home Data Analyst?

AspectFrom Home GpuFrom Home Data Analyst
Required CredentialsKnowledge of GPU hardware, basic troubleshooting skillsDegree in statistics, data analysis, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentRemote technical support or hardware setupRemote data interpretation and reporting
Industry UsageTechnology, gaming, hardware manufacturingFinance, marketing, healthcare, and tech sectors

From Home Gpu roles focus on hardware knowledge and technical support, often in tech-related industries. From Home Data Analyst positions emphasize data interpretation, analysis skills, and reporting, spanning various sectors. Both roles are remote but require different skill sets and industry focus.

More about From Home Gpu jobs

What cities are hiring for From Home Gpu jobs?

Cities with the most From Home Gpu job openings:

What are the most commonly searched types of Gpu jobs?

The most popular types of Gpu jobs are:

What states have the most From Home Gpu jobs?

States with the most job openings for From Home Gpu jobs include:

What job categories do people searching From Home Gpu jobs look for?

The top searched job categories for From Home Gpu jobs are:

Infographic showing various From Home Gpu job openings in the United States as of September 2026, with employment types broken down into 2% As Needed, 76% Full Time, 19% Part Time, and 3% Contract. Highlights an 73% Physical, 1% Hybrid, and 26% Remote job distribution, with an average salary of $53,274 per year, or $25.6 per hour.

GPU Performance Engineer

New York, NY • On-site

Two Sigma Investments, LP
See more jobs at Two Sigma Investments, LPexternal

$165K - $300K/yr

Full-time

Medical, Dental, Life, Retirement, PTO

Re-posted 10 days ago

Key responsibilities

  • Design and implement GPU-accelerated kernels for financial computation workloads

  • Optimize GPU code for throughput, latency, and memory efficiency across current and next-generation hardware

  • Profile and optimize GPU workloads using NVIDIA tooling

Job description

GPU Performance Engineer
Location
NY New York
United States
Business
Investment Management
Function
Engineering
Experience Level
Experienced
Share this job
Position Summary
Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors.
Our team of engineers, quantitative researchers and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world's most complex economic problems.
Two Sigma is building a new team to drive the firm's strategic transition from CPU-centric to GPU-accelerated computation. Accelerated Compute sits within AI Innovation and operates at the intersection of quantitative modeling workflows, GPU performance engineering, and infrastructure strategy.
You are a GPU programming expert. You write CUDA, you optimize kernels, you understand the memory hierarchy, you know why naive GPU code is slow and how to make it fast. You will ensure that when workloads move to GPU, they achieve the performance that justifies the transition.
You will take on the following responsibilities:
  • Design and implement GPU-accelerated kernels for financial computation workloads
  • Optimize GPU code for throughput, latency, and memory efficiency across current and next-generation hardware (Blackwell, Rubin)
  • Develop procedures for precision management (FP8/FP4 training and inference) in financial applications
  • Profile and optimize GPU workloads using NVIDIA tooling (Nsight Systems, Nsight Compute)
  • Build reusable GPU libraries and abstractions that modeling teams can use without requiring deep CUDA expertise
  • Evaluate and integrate GPU-accelerated libraries (RAPIDS, CUTLASS, cuBLAS, TensorRT) for financial use cases

You should possess the following qualifications:
  • BS or MS in Science, Technology, Engineering or Math
  • Minimum 1 year of experience required; 4-10 years of experience preferred
  • Expert-level CUDA programming: kernel development, memory management, stream and graph optimization
  • Deep understanding of GPU architecture: SM structure, warp scheduling, memory hierarchy (registers, shared memory, L1/L2, HBM)
  • Experience with performance profiling and optimization of GPU workloads
  • Strong C++ and Python skills, as well as familiarity with mixed-precision computation and numerical stability
  • Track record of delivering meaningful speedups on real workloads (not just benchmarks)

Preferred experience:
  • Background in HPC, scientific computing, or computational finance
  • Experience with multi-GPU and multi-node GPU programming (NCCL, MPI)
  • Familiarity with GPU-accelerated data processing frameworks (RAPIDS, cuDF)

You will enjoy the following benefits:
  • Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
  • Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
  • Learning: Tuition reimbursement, conference and training sponsorship
  • Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
  • Hybrid Work Policy: Flexible in-office days with budget for home office setup

The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
Two Sigma is committed to providing reasonable accommodations to qualified individuals in accordance with applicable federal, state, and local laws.
If you believe you need an accommodation, please visit our website for additional information.

Apply