Jobs in France

Edge Infrastructure Engineer in Paris

3 active opportunities · Updated October 2026

Explore current edge infrastructure engineer jobs in Paris. Filter by work mode, employment type, experience, department, date posted and distance.

PE
📍 Paris, France· Full-time· Hybrid
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Une entreprise qui transforme le monde qui nous entoure Palantir construit le premier logiciel au monde pour les décisions et les opérations axées sur les données. En fournissant les bonnes données aux personnes qui en ont besoin, nos plateformes permettent à nos partenaires de développer des médicaments vitaux, de prévoir les perturbations de la chaîne d’approvisionnement, de localiser les enfants disparus, etc. Le poste La plateforme Palantir est déployée dans de nombreux environnements de missions critiques, y compris les zones de combat et les réseaux classifiés, de l’arrière d’un Humvee au cloud, en passant par un poste de commandement. Cela signifie opérer dans différents environnements cloud, dans des réseaux isolés physiquement sur site et en périphérie, à grande échelle. Nous recherchons des ingénieur(e)s d’infrastructure Edge pour développer, exploiter et maintenir des services hautes performances, évolutifs et fiables pour notre infrastructure de production. Ce rôle exige une attention particulière aux systèmes bas niveau, y compris le déploiement et la gestion de serveurs physiques « bare metal » dans des centres de données traditionnels et des environnements Edge. Vous serez responsable de l’ingénierie physique du réseau et du développement d’une infrastructure robuste pour garantir les performances de la plateforme Palantir. En plus d’assurer la performance et la fiabilité, vous jouerez un rôle essentiel dans le développement et la mise à l’échelle de nouveaux environnements dans une capacité de déploiement avancé, y compris sur site. Les ingénieur(e)s d’infrastructure Edge combinent une expérience d’ingénierie au niveau du matériel avec la volonté d’améliorer les systèmes existants et la créativité nécessaire pour développer de nouvelles solutions afin de relever des défis en constante évolution. Notre équipe s’efforce d’automatiser les processus dans la mesure du possible en utilisant les outils les mieux adaptés. Nous croyons fermement que les équ

SQLPostgreSQLKubernetesLinux
C
📍 Paris, France· Full-time
✓ Quality checkedCompany trend -100%

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. While tasks are assigned according to everyone’s expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs. In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels. Please Note: We have offices in London, Paris, Toronto, San Francisco, New York but we are also remote-friendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00. As a Member of Technical Staff, you will: Design and write high-performing and scalable software for training models. Develop new tools to support and accelerate research and LLM training. Coordinate with other

PythonKubernetesGitRest
C
📍 Paris, France· Full-time
✓ Quality checkedCompany trend -100%

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Contribute in and provide strong support for model training pipelines, ship state of the art models to production, and bridge the gap between research and production. We have one of the highest ratio of compute to engineers in the world. We do not delineate strongly between engineering and research. Everyone will contribute to writing production code and supporting our research effort depending on individual interest and organizational needs. We have all the compute, data, and talent available for you to do your best work. Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly! There are no restrictions on where you can be located for this role. As a Member of Technical Staff, you will: Design and write high-performant and scalable software for training. Improve our training setup from an infrastructure and codebase performance standpoint. Craft and implement tools to speed up our training cycles and improve the overall efficacy of our training infrastructure Research, implement, and experiment with ideas on our supercompute and data infrastructure

PythonKubernetesGitRest
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