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Cuda Programming Jobs in Newark, NJ (NOW HIRING)

P2P

Campus AI Research Engineer (Full-Time)

Manhattan, NY · On-site

$255 - $345/hr

Work across a mix of programming languages: C / C++ / Python / CUDA and other low‑level GPU languages. * Build large‑scale AI/ML systems that are observable, performant, and flexible. Help ...

New

Baseten

Software Engineer - GPU Kernels

Manhattan, NY · On-site

... CUDA, PTX assembly, and architecture-specific techniques • Apply advanced performance optimization methods such as memory coalescing, warp-level programming, tensor core acceleration, and compute ...

Spiral

Software Engineer - Systems

New York, NY · On-site

$200K - $275K/yr

Strong foundation in operating systems and systems programming * Proven track record in performance ... PyTorch, CUDA) is also a plus * Experience with Rust is a bonus * Willingness to work in-person at ...

P2P

Campus AI Research Engineer (Intern)

Manhattan, NY · On-site

$255 - $345/hr

Work across a mix of programming languages: C / C++ / Python / CUDA and other low-level GPU languages. * Build large-scale AI/ML systems that are observable, performant, and flexible. Help improve ...

New

Pangram

Machine Learning Engineer (Junior)

New York, NY · On-site

$135K - $150K/yr

Experience with NVIDIA GPU programming and CUDA * Experience with distributed training frameworks, such as DeepSpeed, FSDL, Ray * Experience with inference frameworks like vLLM * Experience with ...

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Cuda Programming information

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How much do cuda programming jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for cuda programming in Newark, NJ is $56.84, according to ZipRecruiter salary data. Most workers in this role earn between $46.01 and $66.35 per hour, depending on experience, location, and employer.

What is the difference between Cuda Programming vs GPU Developer?

AspectCuda ProgrammingGPU Developer
Required CredentialsKnowledge of CUDA, C/C++, parallel computingKnowledge of GPU architecture, CUDA, OpenCL, C/C++
Work EnvironmentHigh-performance computing, scientific research, AIGraphics, gaming, scientific visualization, AI
Industry UsageTech companies, research labs, AI firmsGaming, entertainment, tech, research

While Cuda Programming focuses specifically on writing code using NVIDIA's CUDA platform for parallel processing, GPU Developers have a broader role that includes designing, optimizing, and implementing GPU-based solutions across various platforms and technologies. Both roles require knowledge of GPU architecture and programming languages like C/C++, but GPU Developers often work on a wider range of applications beyond CUDA-specific projects.

Are CUDA programmers in demand?

CUDA programmers are in high demand due to the growing need for high-performance computing in fields like artificial intelligence, scientific research, and data processing. Skills in parallel programming, GPU architecture, and CUDA toolkit are highly valued, and job opportunities are expected to grow as industries adopt GPU acceleration for complex tasks.

What does a CUDA programming developer do?

A CUDA programming developer writes software that leverages NVIDIA's CUDA platform to perform parallel processing on GPUs, optimizing computational tasks such as scientific simulations, machine learning, and image processing. They typically work with C++ and CUDA-specific libraries, debugging and optimizing code for high performance in environments that require intensive data processing.

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The top searched job categories for Cuda Programming jobs in Newark, NJ are:

What cities near Newark, NJ are hiring for Cuda Programming jobs?

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Cuda Programming jobs near you
Infographic showing various Cuda Programming job openings in Newark, NJ as of August 2026, with employment types broken down into 1% Internship, 79% Full Time, 14% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $118,230 per year, or $56.8 per hour.

Machine Learning Performance Engineer

Trading Interview

Manhattan, NY • On-site

$170 - $210/hr

Other

Posted 4 days ago

Job description

We are looking for an engineer with experience in low-level systems programming and optimisation to join our growing ML team.

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.

Your part here is optimising the performance of our models – both training and inference. We care about efficient large-scale training, low-latency inference in real-time systems and high-throughput inference in research. Part of this is improving straightforward CUDA, but the interesting part needs a whole-systems approach, including storage systems, networking and host- and GPU-level considerations. Zooming in, we also want to ensure our platform makes sense even at the lowest level – is all that throughput actually goodput? Does loading that vector from the L2 cache really take that long?

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in.

There’s no fixed set of skills, but here are some of the things we’re looking for:

  • An understanding of modern ML techniques and toolsets
  • The experience and systems knowledge required to debug a training run’s performance end to end
  • Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy
  • Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and nsight-compute
  • Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS
  • Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization and asynchronous memory loads
  • Background in Infiniband, RoCE, GPUDirect, PXN, rail optimisation and NVLink, and how to use these networking technologies to link up GPU clusters
  • An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI
  • An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools

We were founded by a small group of traders and technologists in a tiny New York office. Today, we have more than 2,000 employees across five global offices. We trade…

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