← Back to list
Job · Principal

Principal ML Platform Engineer

Synthesia
AI / ML Engineer • Principal • Remote • Full-time • 📍 Europe
📄 View original listing (EN)

Synthesia, the world's leading AI video platform for business (used by over 90% of the Fortune 100), is hiring a Principal Engineer for its ML Platform team. The team builds and operates the systems that let researchers and product teams train, serve, and deploy generative models reliably and efficiently, including research infrastructure, production serving systems, internal tooling, and platform interfaces, with a growing focus on automation-friendly and agent-oriented workflows. This is a hands-on individual-contributor role with significant ownership for a strong generalist with a systems mindset; it is not a pure ML Engineer role.

Stack

GitHubKubernetesLinuxPythonTerraform

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 workloads running on GPUs and cloud infrastructure are scheduled, monitored, and debugged
  • ▹Develop internal tools, 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 turn pain points 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: thinking 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 focused on solving the right problem well rather than over-engineering

Nice to have

  • ▹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
  • ▹Experience building agentic or LLM-powered internal tools
  • ▹Experience with workflow orchestration systems such as Temporal
  • ▹Experience working at the boundary between research and production engineering
  • ▹Familiarity with performance optimization, scheduling, or resource allocation problems
  • ▹Experience building lightweight product or developer-facing tools

Soft skills

Strong ownership and ability to work as a hands-on individual contributorComfort with ambiguityPragmatism and good engineering judgmentCollaboration with researchers and product engineers