This role pays more than
82%
of similar roles. Most pay
$170,000–$235,750
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$203k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Senior DevTech Compute Engineer, Compression and Data Processing joins the Developer Technology Compute team to prototype and integrate novel approaches for GPU-accelerated distributed data processing. The role involves developing high-throughput, low-latency advanced lossless and lossy compression methods, transactional and vector databases, and dataframe analytics. You will collaborate with experts to optimize complex data-intensive workloads across heterogeneous GPU/CPU architectures while influencing the design of next-generation hardware, software, and programming models. Required skills include fluency in C/C++, mastery of algorithms and data structures, and experience with low-level parallel programming using CUDA, ROCm, Metal, OpenACC, OpenMP, MPI, pthreads, or TBB. The role addresses technical challenges in "on-the-wire" compression, memory management, and distributed data processing for domains like large-scale data lakes, RL training runs, and multi-stage overlapped workloads involving columnar row-groups, DL tensors, and multidimensional media.
Prototype and integrate novel approaches for GPU-accelerated distributed data processing and dataframe analytics.
Develop high-throughput, low-latency advanced lossless and lossy compression methods.
Optimize complex data-intensive workloads for heterogeneous GPU/CPU architectures.
Co-design next-generation hardware architectures, software components, and programming models.
Collaborate with industry and academic experts to perform in-depth analysis of system bottlenecks.
Work with major customers and CSPs to integrate solutions and influence open standards in data analytics.
Develop advanced features for transactional databases, vector databases, and ETL pipelines.
What we're looking for
Master's or PhD in Computer Science, Computer Engineering, Applied Math, or a related computationally focused science degree (or equivalent experience).
At least 5+ years of relevant work or research experience with a track record in state-of-the-art systems or complex projects.
Hands-on experience with low-level parallel programming across CPU, GPU, NPU, and ASIC execution units using frameworks like CUDA, ROCm, Metal, OpenACC, OpenMP, MPI, pthreads, or TBB.
Fluency in C/C++, algorithms, data structures, and accelerator architecture fundamentals including memory subsystems, caches, NICs, and storage I/O.
Domain expertise in data processing, compression and decompression, codecs, high-performance distributed databases, ETL, and data analytics.
Background in specific technologies like lossy/lossless compression (ANS, Bitpack), video/image codecs (H.264, H.265, AV1, ProRes), or networking (preferred).
Track record of zero-to-one projects involving multiple stakeholders resulting in substantial TCO gains or new workflows (preferred).
Open-source contributions or committee participation in the related domain and fields (preferred).
C++
C
x86
ARM
GPU
Parallel Programming
Vectorization
CPU Intrinsics
Concurrent Data Structures
Spark
HPC
Accelerated Computing
Memory Subsystem
Data Analytics
Algorithms
Storage Systems
Networking
CUDA
C++
C
OpenACC
OpenMP
MPI
pthreads
TBB
GPU Architecture
High-Performance Databases
ETL
Spark
Memory Management
Parallel Programming
Data Analytics
Storage Systems
Networking
C++
C
CUDA
OpenACC
OpenMP
MPI
pthreads
Deep Learning
Machine Learning
HPC
GPU Architecture
Parallel Programming
Linear Algebra
Computer Vision
Natural Language Processing
CUDA
C++
C
OpenMP
MPI
NVSHMEM
High-Performance Computing
Machine Learning
Deep Learning
Nsight Systems
Nsight Compute
Parallel Programming
GPU Architecture
Data Structures
Graph Theory
Computational Fluid Dynamics
Deep Learning
Machine Learning
LLMs
Computer Vision
CUDA
C++
C
OpenMP
MPI
pthreads
GPU Architecture
Parallel Programming
Linear Algebra
Performance Optimization
Multimodal Architectures