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Field Application Engineer - AI Software & Hardware

CompraTica Empleos

EMP:Technology
Munich
Tiempo Completo
Remoto
0 vistas

Descripción

<div class="content-intro"><p>Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency.

With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors.

Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible.

We value collaboration, curiosity, and a commitment to solving hard problems.

We are growing our team and looking for contributors of all seniorities.

</p></div><p>We’re looking for a Field Application Engineer who’s wired for AI/ML, fluent in real-world problem-solving, and excited to build with the people actually using what we make.

You will collaborate closely with the sales team and enterprise customers, leveraging your deep technical knowledge in AI to drive the adoption of our products and solutions.

This is a customer-facing role that requires both technical expertise and excellent communication.

Habilidades

to convey complex technical concepts to non-technical stakeholders.

This role is remote based out of Munich, Germany; Poland or Serbia.  

We welcome candidates at various experience levels for this role

During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

</em></p> <p> </p> <p><strong>Who You Are</strong></p> <ul> <li>You’ve lived in the AI/ML trenches, whether as a field engineer, a solutions architect, or the one tapped in when things needed to “just work.

”</li> <li>You speak both machine and human.

Whether it’s a researcher or a skeptical executive, you know how to break things down and bring them to life.

</li> <li>You’re fired up.

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generative models, LLMs, and the edge of what’s possi...

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