About the job
We’re looking for an AI Developer Technology Engineer to push the limits of performance at the intersection of AI, high-performance computing, and financial markets. In this role, you’ll dive deep into parallel algorithms, GPUs, and complex systems to identify and eliminate bottlenecks, unlocking the full power of the world’s most advanced processing hardware. You’ll collaborate with top experts across industry and academia, influence next-generation platforms, and share your insights with the global developer community.
Responsibilities
Researching, designing, and developing groundbreaking techniques to accelerate high-performance workloads for FSI-focused, pioneering AI on NVIDIA CPUs and GPUs.
Working 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.
Qualifications
Minimum
Pursuing or recently completed a Master’s or PhD degree (or equivalent experience) in Computer Science, Computer Engineering, or Electrical and Computer Engineering or related field.
Relevant work or research experience.
Experience with low-level parallel programming (e.g., CUDA).
Deep understanding of CPU/GPU architecture fundamentals and how they impact performance.
Fluency in C/C++ and solid foundations in algorithms and software design.
Experience improving the performance of large-scale computational applications on GPUs.
Good understanding of linear algebra.
Strong communication and organization skills, with a logical approach to problem solving and solid prioritization abilities.
Preferred
Prior internship experience in a related field.
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 as well as knowledge of financial data models, pricing and risk simulation algorithms, portfolio optimization, or other finance-focused applications and services.