Principal ML Platform Engineer

The role

A Principal Engineer will build and operate systems that allow researchers and product teams to train, serve, and deploy generative models. This role involves designing platform systems, infrastructure, and internal tooling to improve reliability, scalability, and efficiency.

What you will do

• Design and improve platform systems for model training, evaluation, and production serving.
• Build infrastructure and tooling for reliable, scalable, and cost-efficient ML workloads.
• Develop internal tools and workflows for humans and agents.
• Work on architecture for model deployment, serving, and operation.
• Improve scheduling, monitoring, and debugging for GPU and cloud infrastructure workloads.
• Develop internal tools, abstractions, and agentic systems to reduce operational overhead.
• Drive improvements in observability, automation, reliability, and developer experience.
• Collaborate with researchers and product engineers to create platform capabilities.
• Contribute to technical direction and architectural tradeoffs.

Required qualifications

• Experience building or operating production systems with a focus on reliability, scalability, and maintainability.
• Systems mindset regarding bottlenecks, failure modes, interfaces, resource usage, and long-term operability.
• 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 regarding automation and human control.
• Experience building internal platforms, developer tooling, or infrastructure abstractions.
• Comfort working in ambiguous environments and taking ownership of open-ended technical problems.
• Pragmatic approach to solving problems.
• 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.

Nice to have

• 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.

About the company

Synthesia is an AI video platform for business used by over 90% of the Fortune 100. The company is headquartered in London with offices and teams across Europe and the US.