ML Platform Engineer

🕒 5 days ago

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

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Synthesia

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2017

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

🔥 Funding within the last year

💰 $200M Series E - Synthesia on 2025-10

Artificial Intelligence • SaaS • B2B

<Synthesia> Synthesia is a SaaS AI video platform that enables businesses to create studio-quality videos without cameras, microphones, actors, or studios by using AI avatars and synthetic voiceovers. The platform supports 160+ languages, one-click translation/localization, an AI screen recorder, brand management, collaboration and analytics, and enterprise-grade security (SOC 2 Type II, GDPR). It’s marketed primarily to teams and enterprises for training, sales enablement, marketing, knowledge management and internal communications, helping companies scale video production while reducing time and cost.

📋 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Health insurance • Retirement plans • Flexible work arrangements • Professional development