Principal ML Platform Engineer - remote job

Synthesia is hiring a Principal ML Platform Engineer - a lead role, which means responsibility for a team or a direction. The role sits in IT and programming and is fully remote. The company hires candidates based in Europe.

The advert singles out Machine Learning, so experience with that one tool is what will decide it.

The employer did not publish a figure; that is settled at interview. The advert sets no condition on working hours.

This vacancy passed an automated check: the list excludes any advert requiring a foreign work permit, visa sponsorship, a particular citizenship, or residence in a specific country.

Synthesia, a pioneering AI video platform, serves more than 90% of the Fortune 100. Established in 2017, the company has a presence in London and across Europe and the United States.

With AI transforming the landscape of business and communication, Synthesia focuses on enhancing visual content and enterprise skill development, aiming to optimize collaboration and success in organizations.

After a recent Series E funding round, Synthesia secured $200 million, bringing its valuation to $4 billion. The company has raised over $530 million from leading investors like Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, as well as founders and operators from Stripe, Datadog, Miro, and Webflow.

We are seeking a Principal Engineer to join Synthesia's ML Platform team.

Our team crafts and manages the infrastructure that enables research and product teams to train, serve, and deploy generative models efficiently. This involves developing research tools, production systems, and user interfaces that integrate these components. A significant part of our mission is to make these systems more automated and agent-oriented, ensuring workflows can operate reliably with minimal manual intervention.

We are seeking a versatile engineer with a systems perspective:

- Someone who can work seamlessly across infrastructure, backend systems, and tooling, and who has practical experience with ML systems. - This role is not just about ML engineering; we're particularly interested in individuals who deeply understand reliability, scalability, performance, and resource efficiency in complex production environments. - This is a hands-on, impactful role with significant responsibility. You will help shape the evolution of our ML platform as we scale the number of models, workloads, tools, and teams relying on it.

What you will do:

- Design and enhance the platform systems that support model training, evaluation, and production serving. - Build infrastructure and tooling that ensure ML workloads are more reliable, scalable, and cost-efficient. - Develop internal tools and workflows that are both easy for humans to operate and suitable for automation. - Work on the architecture behind model deployment, serving, and operation across research and product environments. - Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure. - Develop internal tools and abstractions that reduce operational overhead for researchers and engineers. - Drive improvements in observability, automation, reliability, and developer experience. - Collaborate closely with researchers and product engineers to identify pain points and transform them into robust platform capabilities. - Contribute to technical direction and make practical architectural decisions as the platform grows.

You will thrive in this role if you have:

- Extensive experience building or operating production systems with a focus on reliability, scalability, and maintainability. - A systems mindset: you naturally think about 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 managing distributed workloads in production. - Strong coding skills, preferably in Python or similar languages used for backend systems and tooling. - Strong judgment on where automation adds value and where human oversight is crucial. - 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 challenges. - A pragmatic approach: you prioritize solving the right problem well and avoid over-engineering.

Relevant experience:

- 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.

Additional benefits:

- Experience building agentic or LLM-powered internal tools. - Experience with workflow orchestration systems like Temporal. - Experience working at the intersection of research and production engineering. - Familiarity with performance optimization, scheduling, or resource allocation challenges. - Experience building lightweight product or developer-facing tools.