About The Position
It’s an exciting time for NVIDIA as we expand our capabilities into the world of data science, data processing, and database acceleration. We're looking for an outstanding software engineer to apply their skills in the development of our unstructured data processing libraries, NVIDIA cuVS and cuML. These libraries compile highly optimized C++ and CUDA algorithms and expose them to higher-level languages like Java and Python. These higher-level languages are then used directly or to accelerate other libraries and database systems. In this role, you will research, develop, benchmark, and explore novel tuned custom algorithms for accelerating vector preprocessing, indexing, and search, as well as building high-performance ML algorithms for clustering and visualization. This is a great chance to take advantage of your CUDA/C++ skills and make a huge impact across a rapidly growing industry. Vector search is an exciting new field at the intersection of database and machine learning. You’ll work closely with a team of stellar engineers redefining what’s possible.
Requirements
- BS, MS, or PhD in Computer Science, Data Science or AI, Applied Math, or related field (or equivalent experience).
- 8+ years of experience programming in C++ or ability to learn it, by moving from a similar language: C, Rust, Java
- Experience with ML: ML concepts (approximation vs exact, iterative vs. closed-form), writing ML algorithms, and using ML to solve problems.
- Strong analytical problem-solving skills, algorithms and mathematics fundamentals.
- Excellent software development skills: programming, debugging, performance analysis, and test design
- Ability to work independently and manage your own development efforts.
- Good communication and documentation habits.
- You care deeply about robust, readable, high-performance code
- Familiar with at least one parallel programming or concurrency framework, such as CUDA, OpenMP, OpenACC, Java concurrency, pthreads, etc
Nice To Haves
- Experience developing distributed algorithms and running on distributed systems: HPC, Cloud, etc
- Strong debugging skills, including for complex multi-language, multi-hardware systems.
- Familiarity with vector databases and nearest-neighbor algorithms (for example, Milvus, Pinecone, LanceDB; graph and IVF indexes).
- ML background, including clustering and dimensionality reduction; probability and formal methods; applied research
- GPU programming is a plus, though we’re happy to teach GPU development.
Responsibilities
- Analyze, design, and implement optimized GPU algorithms for large-scale vector search, databases, and machine learning.
- Drive performance analysis, benchmarking, and optimization of associated libraries.
- Collaborate with a multi-functional team to understand requirements and implement or improve solutions
- Developing and improving machine learning algorithms.
- Implementing solutions in C++, CUDA and Python.
- Contributing to open source projects (like cuML, cuVS, and RAFT)
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
