About the job
We’re currently seeking a Senior AI Developer Technology Engineer, Financial Sector! Would you like to help shape the future of financial AI and data analytics by designing and optimizing parallel algorithms on cutting-edge computing platforms? Is it rewarding to investigate, find, and eliminate system bottlenecks to achieve the best possible performance of computer hardware? Could you be thrilled about an opportunity to partner with the Developer community, working at the forefront of technology breakthroughs that contribute to the success of an industry leader like NVIDIA? If so, the Developer Technology Team invites you to consider this role.
Responsibilities
In this position, you will research and develop techniques to GPU-accelerate high-performance workloads at the intersection of AI and financial markets.
Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex AI and HPC workloads to ensure the best possible performance on modern CPU and GPU architectures.
Publish and present discovered optimization techniques in developer blogs or relevant conferences to engage and educate the Developer community.
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.
Qualifications
Minimum
An advanced degree in Computer Science, Computer Engineering, or related computationally focused science degree (or equivalent experience).
You have 5+ years of relevant work or research experience.
Direct experience improving the performance of large computational applications used by financial institutions.
Excellent understanding of linear algebra.
Programming fluency in C/C++ with a deep understanding of algorithms and software design.
Hands-on experience with low-level parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, TBB, etc.
In-depth expertise with CPU/GPU architecture fundamentals.
Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.
Preferred
A Master’s or PhD in a relevant field is highly valued.
Prior work experience in capital markets with exposure to systematic/algorithmic strategies and quantitative trading.
Experience with parallelizing and optimizing machine learning algorithms like decision trees, time series, and Monte Carlo simulations.
Deep knowledge of financial data models, pricing/risk simulation algorithms, portfolio optimization, or other financial specific applications/services.
Have developed ML/DL techniques in the finance space, such as stock market prediction, fraud detection, portfolio optimization/selection.