ML Platform Engineer

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100.

As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.

We're looking for an Engineer to join the ML Platform team at Synthesia.

Our team builds and operates the systems that allow researchers and product teams to train, serve, and deploy generative models reliably and efficiently. This includes research infrastructure, production serving systems, internal tooling, and the platform interfaces that connect them.

What you’ll do

• 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.

You’ll thrive in this role if you have

• 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.