3 meilleures offres (stack developer) (à pourvoir)

Senior Solutions Architect, Higher Education and Research, Multimodal and Physical AI
NVIDIA —Zürich, ZH
- 100%
- Niveau Doctorat
- Anglais
- Recherche
15 h
Staff Platform Engineer
Nango —Suisse
- De 140 000 CHF à 220 000 CHF par an
- 100%
- Télétravail
- AWS
- Technologies de l'information
- Compétences en communication
il y a 7 jours
Software Engineer III, Original Content, YouTube
YouTube —Zürich, ZH
- 100%
- C++
- Niveau Licence
- Sans diplôme d'enseignement supérieur
8 h

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Senior Solutions Architect, Higher Education and Research, Multimodal and Physical AI

NVIDIA -
Zürich, ZH

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Détails de l'emploi

100% | Temps de travail : 90-100%
Il y a 15 heures

Qualifications

Niveau Doctorat
Anglais
Recherche
Compétences en communication
Python

Description complète du poste

NVIDIA has been redefining computer graphics and accelerated computing for three decades. Today, we are tapping into the unlimited potential of AI to define the next era of computing. Doing what has never been done before takes vision, innovation, and exceptional talent. As an NVIDIAN, you will be immersed in a diverse, supportive environment where everyone is encouraged to do their best work and make a lasting impact on the world.
We are looking for a Solutions Architect to work with academia and research partners. Solutions Architects are the primary technical contacts for our customers and engage deeply with researchers and application developers to drive the adoption of NVIDIA technology. We seek an individual who combines an intricate understanding of Multimodal AI and Physical AI with expertise in accelerated computing and architecture.

What you will be doing:

Partner directly with leading research labs, researchers, and university faculty as a trusted technical advisor: develop a keen understanding of their scientific goals and drive joint research projects at supercomputing scale.
Identify and accelerate high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects, and help researchers amplify their impact through publications, conference presentations, and technical content.
Advocate for accelerated computing, robotics, Multimodal AI and Physical AI, and deliver hands-on trainings, workshops, lectures and demonstrations across NVIDIA's platforms, and mentor power users to become NVIDIA champions.
Track emerging research trends and turn gaps between researcher needs and NVIDIA's offerings into prototypical solutions and direct feedback to NVIDIA Engineering.
Maintain deep expertise in your domain while staying versatile across NVIDIA's full platform: GPUs, CPUs, networking, and software

What we need to see:

A graduate degree from a leading university in a STEM related discipline or equivalent experience.
5+ years across the multimodal and world model lifecycle on multi-node GPU systems: video and image data curation at scale, pre-training and post-training, evaluation, and efficient inference.
Strong collaboration and communication skills, with the ability to build relationships with academic and research stakeholders, and to communicate complex ideas clearly to both expert and non-expert audiences.
Action oriented, analytical, self-motivated, and a passionate learner, with excellent organization skills to work in a heavily multi-tasked environment.
Fluent in English, both oral and written, and comfortable working in Python

Ways to stand out from the crowd:

A PhD from a leading university in a STEM related discipline, with 3+ years of research in the domain.
A track record of thought leadership: high-impact publications, talks at academic conferences and workshops, as well as good understanding of scientific policy engagement, grant processes, or national/European research program structures.
Experience with NVIDIA's stack for visual and multimodal AI, built on CUDA and CUDA-X, including Cosmos world foundation models, NeMo Framework and Megatron for multimodal model training, multimodal Nemotron methodology, Isaac robotics platform, NuRec for neural reconstruction, TensorRT and NIM for deployment, and domain frameworks such as MONAI for medical imaging.

As a Solutions Architect, there is travel involved (20% of the working time), as often the best way to figure things out is a face-toface meeting, but the job is not life on the road. We make heavy use of conferencing tools, and you are empowered to figure out how to get the job done and do what it takes to make our customers successful

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Senior Solutions Architect, Higher Education and Research, Multimodal and Physical AI

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Software Engineer III, Original Content, YouTube

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