Description
We're looking for a Senior AI Developer Technology Engineer to push the limits of performance at the intersection of AI, high-performance computing, and financial markets. You'll dive deep into parallel algorithms, GPUs, and sophisticated systems, identifying and eliminating bottlenecks to unlock the full power of the most advanced processing hardware in the world.
You'll collaborate with top experts across industry and academia, influence next-generation platforms, and share your insights with the global developer community. Would you enjoy solving hard technical problems, love performance tuning, and want your work to have a visible impact across an entire industry? If so, we would love to invite you to consider this role.
Responsibilities:
- Researching, designing, and developing groundbreaking techniques to accelerate high-performance workloads for FSI-focused, pioneering AI on NVIDIA CPUs and GPUs.
- Working hands-on with leading technical experts to analyze, optimize, and scale complex AI and HPC workloads for modern CPU and GPU architectures.
- Profiling and eliminating performance bottlenecks across the stack: from algorithms to kernels to system-level behavior.
- Publishing and presenting your work in conferences, talks, and blogs to educate and inspire the broader developer community.
- Influencing the design of future hardware architectures, system software, libraries, and programming models by collaborating closely with NVIDIA research, hardware, compiler, and tools teams.
Requirements:
- Master's or PhD in Computer Science, Computer Engineering, or Electrical and Computer Engineering (or equivalent experience).
- Strong, hands-on experience with low-level parallel programming (e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, TBB, etc.).
- Deep understanding of CPU/GPU architecture fundamentals and how they impact performance.
- Fluency in C/C++ and solid foundations in algorithms and software design.
- 5+ years of relevant work or research experience.
- Proven experience improving the performance of large-scale computational applications on GPUs.
- Excellent understanding of linear algebra.
- Strong communication and organization skills, with a logical approach to problem solving and solid prioritization abilities.
Nice to Have:
- Experience with inference optimization techniques and deploying optimized AI models in production.
- Experience with TensorRT, TensorRT-LLM, and cuTile.
- Background in capital markets with exposure to systematic/algorithmic strategies or quantitative trading.
- Experience parallelizing and optimizing machine learning methods such as decision trees, time series models, and Monte Carlo simulations.
- Knowledge of financial data models, pricing and risk simulation algorithms, portfolio optimization, or other finance-focused applications and services.