Healthcare Computing Developer Technology Engineer

Il y a 2 jours

Courbevoie, Île-de-France NVIDIA Temps plein 70 000 € - 90 000 € Contrat

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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.

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, OpenMP or OpenACC.
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.

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