Senior ML Platform Engineer

Emploi pas sur LinkedIn

🕒 il y a 4 mois

🇬🇧 Royaume-Uni – Télétravail

⏰ Temps Plein

🟠 Senior

🏗️ Ingénieur Plateforme

🇬🇧 Parrain de Visa de Travailleur Qualifié UK

Cette entreprise a parrainé des visas de Travailleur Qualifié du Royaume-Uni par le passé.

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Synthesia

Site WebLinkedInTous les Emplois

501 - 1000 employés

Fondée en 2017

📣 Marketing

💼 Conseil

📦 Logistique

🔥 Financement dans la dernière année

💰 €200 000 000 Series E - Synthesia en 2025-10

Marketing • Consulting • Logistics

<Synthesia> Synthesia est une plateforme SaaS de vidéos IA qui permet aux entreprises de créer des vidéos de qualité studio sans caméras, microphones, acteurs ou studios en utilisant des avatars IA et des voix off synthétiques. La plateforme prend en charge plus de 160 langues, offre une fonction de traduction/localisation en un clic, un enregistreur d'écran IA, la gestion de marque, la collaboration et l'analyse, ainsi qu'une sécurité de niveau entreprise (SOC 2 Type II, RGPD). Elle est principalement commercialisée auprès des équipes et des entreprises pour la formation, l'aide à la vente, le marketing, la gestion des connaissances et la communication interne, aidant les entreprises à accroître la production vidéo tout en réduisant le temps et les coûts.

Description

• Design and improve the platform systems that support model training, evaluation, and production serving. • Build infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient. • Develop internal tools and workflows that are easy to operate both by humans and by agents. • Work on the architecture behind how models are deployed, served, and operated across research and product environments. • Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure. • Develop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers. • Drive improvements across observability, automation, reliability, and developer experience. • Collaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities. • Contribute to technical direction and make pragmatic architectural tradeoffs as the platform grows.

🎯 Exigences

• Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability. • A systems mindset: you naturally think in terms of bottlenecks, failure modes, interfaces, resource usage, and long-term operability. • Solid hands-on experience with cloud infrastructure, Linux, and infrastructure automation. • Experience with Kubernetes and operating distributed workloads in production. • Strong coding skills, ideally in Python or similar languages used for backend systems and tooling. • Strong judgment around where automation adds leverage, and where human control and reliability matter most. • Experience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers. • Comfort working in ambiguous environments and taking ownership of open-ended technical problems. • A pragmatic approach: you care about solving the right problem well, not over-engineering. • Operating ML infrastructure or model serving systems in production. • Supporting research or data-intensive workloads. • Working with GPU-based systems or other performance-sensitive infrastructure. • Experience with observability and debugging in distributed systems. • Familiarity with Terraform, Datadog, GitHub Actions, or similar tools.

🏖️ Avantages

• Health insurance • Flexible working hours • Professional development opportunities