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NVIDIA

Senior DevTech Compute Engineer, Compression and Data Processing

Reposted 2 Days Ago
Remote or Hybrid
2 Locations
184K-357K Annually
Senior level
Remote or Hybrid
2 Locations
184K-357K Annually
Senior level
Prototype and integrate GPU-accelerated distributed data processing, compression, databases, and analytics solutions. Optimize complex data-intensive workloads across heterogeneous CPU/GPU architectures, collaborate on next-generation hardware and software designs, and work with customers and cloud service providers to deploy solutions and influence open standards. The role requires deep expertise in parallel programming, C/C++, accelerator architecture, memory systems, and compression or distributed data systems.
The summary above was generated by AI

NVIDIA is currently seeking a highly motivated Senior DevTech Compute Engineer for Compression and Data Processing! Would you enjoy prototyping and developing ground breaking methods and data formats to accelerate complex distributed workflows? Do you love investigating and overcoming system-level bottlenecks for multi-stage and multi-IP overlapped workloads? Do you prefer squeezing all useful entropy out of data, whether it is columnar row-groups, DL tensors or multidimensional images and videos? Are you excited about co-designing the systems, software components and hardware blocks to define the next frontier for distributed data processing? If so, this is an outstanding opportunity for you to join the Developer Technology Compute team.


Data analytics, databases and distributed data processing is one of the fastest growing domains for non-CPU accelerated computing: “on-the-wire” compression and decompression is already part of the switches and DPUs, low-latency queries across huge amounts of data in data lakes at scale in the middle of the RL training run defines the business agility. Here at NVIDIA Devtech Compute team we take a holistic approach to data movement, late materialization, memory management and spilling, parallel algorithms, collectives and compression and quantization. Read more from the team: Designing GPU Query Engines, Cut Checkpoints Costs or take a look at some of the projects in depth: NVIDIA nvCOMP, NVIDIA GPU Query Engine (GQE), NVIDIA cuCollections.


What you will be doing:

  • In this role, you will prototype and integrate novel approaches to GPU-accelerated distributed data processing domains: dataframe analytics, high-throughput low-latency advanced lossless and lossy compression methods, transactional and vector databases.
  • Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex data intensive workloads to ensure the best possible performance of current heterogeneous GPU/CPU architectures.
  • Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA.
  • Work directly with the NVIDIA largest customers and CSPs to integrate the solutions at Speed-Of-Light, and influence open standards in data analytics and compression

What we need to see:

  • Masters or PhD in Computer Science, Computer Engineering, Applied Math and/or related computationally focused science degree (or equivalent experience).
  • At least 5+ years of relevant work or research experience, with a track record in the state-of-the-art systems or complex projects, involving cross-team collaboration and solid prioritization skills.
  • Hands-on experience with low-level parallel programming across execution units (CPU/GPU/NPU/ASICs), e.g., CUDA, ROCm, Metal, OpenACC, OpenMP, MPI, pthreads, TBB, etc.
  • Fluency in C/C++, algorithms and data structures
  • CPU/GPU/NPU accelerators architecture fundamentals, memory subsystem, caches, NICs and storage I/O
  • Domain expertise in data processing, compression and decompression, codecs or in high performance distributed databases, ETL and data analytics

Ways to stand out from the crowd:

  • PhD or a recent project/publication in a relevant field.
  • Background in compression (lossless/lossy, ANS, Bitpack), video or image codecs (H.264, H.265, AV1, ProRes), low-latency data analysis, storage systems, networking, and distributed computer architectures.
  • Track of records in zero-to-one project or initiatives, spanning several stakeholders and resulting in substantial TCO gains or enabling new workflows.
  • Open-source contributions or committee participation in the related domain and fields.
  • Excellent interpersonal skills, problem solving, and the ability to communicate efficiently in sophisticated technical scenarios

NVIDIA is recognized as one of the most desirable employers. We’re honored that Glassdoor has named our founder and CEO, Jensen Huang, No. 1 on its 2026 Best CEOs list. In addition, we have some of the most forward-thinking and hardworking people in the world working here. If you're ambitious, creative, and autonomous, come join us and contribute to a team that is pushing the edges of what can be done in AI.


#LI-Hybrid

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

HQ

NVIDIA Santa Clara, California, USA Office

2701 San Tomas Expressway, Santa Clara, CA, United States, Santa Clara

NVIDIA San Francisco, California, USA Office

San Francisco, United States

NVIDIA San Jose, California, USA Office

San Jose, United States

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