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Principal ML Platform Engineer

Synthesia· Research and Development
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RemoteFull-timePosted 6mo ago

About the role

Principal Engineer to join the ML Platform team at Synthesia, building and operating systems that allow researchers and product teams to train, serve, and deploy generative models reliably and efficiently. Focus on reliability, scalability, performance, and resource efficiency in production environments; ownership role shaping platform evolution as models and workloads scale.

Responsibilities

  • 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.

Qualifications

  • Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability.
  • A systems mindset: bottlenecks, failure modes, interfaces, resource usage, 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: solving the right problem well, not over-engineering.

Skills

Required:Production systemsCloud infrastructureLinuxInfrastructure automationKubernetesPython

Benefits

  • Not specified in the description
Last updated Jul 15, 2026. Interested? Apply on the company site.

About Synthesia

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