Healthcare Computing Developer Technology Engineer

What you will be doing:

• Work directly with other technical experts in the domain (industry and academia) to understand and address their computational problems as the domain evolves.
• Perform in-depth analysis and optimization to ensure the best possible performance on the current and next generation of NVIDIA GPUs, CPUs (Arm and x86 architectures), and/or network architectures.
• Craft and optimize core parallel algorithms and data structures to provide the best solutions using the NVIDIA platform.
• Guide key application developers, contribute directly to the applications, and develop reference codes and libraries.
• 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.
• Occasional travel for conferences and on‑site visits with developers.

What we need to see:

• A Bachelors, Masters or PhD (or equivalent experience) in Computer Engineering, Computer Science, or a domain science with a strong focus on the related computational methods.
• 5+ years of meaningful work experience.
• Familiarity with bioinformatics or medical imaging concepts and techniques.
• Programming proficiency in C/C++ with a deep understanding of algorithms, programming techniques, and software design.
• Hands‑on and current experience with parallel programming, ideally CUDA, C++ standard parallelism, Open

  MP or Open

  ACC.
• Strong mathematical fundamentals, including linear algebra and numerical methods.
• Good communication and organization skills, with a logical approach to problem solving, good time management, and task prioritization skills.

Ways to stand out from the crowd:

• Medical imaging algorithms, e.g. CT, MRI, and Ultrasound reconstruction, point‑cloud registration, 3D semantic segmentation.
• Bioinformatic methods, e.g. genome assembly, variant calling, GWAS, single‑cell analysis, sequence alignment, spatial genomics, base‑calling.
• Other fields of bioinformatics, e.g. proteomics, bisulfite sequencing, pangenomics, chemo‑informatics, drug discovery.
• Deep Learning methods used in medical imaging or bioinformatics fields