Senior AI Developer Technology Engineer, Financial Sector

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +3 · Remote

Senior AI Developer Technology Engineer focused on optimizing AI and HPC workloads for financial markets on NVIDIA's computing platforms. This role involves research, development, performance analysis, and collaboration with the developer community and internal teams to influence hardware and software design.

What you'd actually do

  1. In this position, you will research and develop techniques to GPU-accelerate high-performance workloads at the intersection of AI and financial markets.
  2. 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.
  3. Publish and present discovered optimization techniques in developer blogs or relevant conferences to engage and educate the Developer community.
  4. 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.

Skills

Required

  • C/C++
  • algorithms
  • software design
  • low-level parallel programming
  • CUDA
  • OpenACC
  • OpenMP
  • MPI
  • pthreads
  • TBB
  • CPU/GPU architecture fundamentals
  • linear algebra

Nice to have

  • Master’s or PhD in a relevant field
  • capital markets
  • systematic/algorithmic strategies
  • quantitative trading
  • parallelizing and optimizing machine learning algorithms
  • decision trees
  • time series
  • Monte Carlo simulations
  • financial data models
  • pricing/risk simulation algorithms
  • portfolio optimization
  • stock market prediction
  • fraud detection
  • portfolio optimization/selection

What the JD emphasized

  • Direct experience improving the performance of large computational applications used by financial institutions.
  • 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.

Other signals

  • GPU-accelerate high-performance workloads
  • AI and financial markets
  • optimization of complex AI and HPC workloads
  • performance on modern CPU and GPU architectures