About the job
Join Synthesia, the world's premier AI video platform used by over 90% of the Fortune 100 companies. Since our inception in 2017, we have established our headquarters in London and expanded our reach across Europe and the United States.
As artificial intelligence continues to revolutionize our daily lives and workplaces, Synthesia is at the forefront of developing innovative products that enhance visual communication and foster enterprise skill development, enabling people to excel and remain pivotal in thriving organizations.
Following our recent Series E funding round, where we successfully raised $200 million, our company valuation has soared to $4 billion, with total funding surpassing $530 million from esteemed investors such as Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside founders and operators of leading firms like Stripe, Datadog, Miro, and Webflow.
We are seeking a talented Senior Machine Learning Platform Engineer to become a vital part of our ML Platform team.
In this role, you will be responsible for building and maintaining the systems that empower researchers and product teams to train, serve, and deploy generative models efficiently and reliably. Your responsibilities will span research infrastructure, production serving systems, internal tooling, and the platform interfaces that unify them. A key focus will be on enhancing the automation and agent-oriented capabilities of these systems, allowing workflows to be executed through dependable tools rather than manual processes.
We are looking for a well-rounded engineer with a systems-oriented mindset:
Someone who has experience in infrastructure, backend systems, and tooling, and has practical exposure to ML systems.
This is not a conventional ML Engineer position. We seek individuals who are deeply invested in reliability, scalability, performance, and resource efficiency within complex production settings.
This is a hands-on senior individual contributor role with significant ownership over how our ML platform evolves as we scale the number of models, workloads, tools, and teams that depend on it.
Key Responsibilities
Design and enhance the platform systems supporting model training, evaluation, and production serving.
Construct infrastructure and tools that bolster the reliability, scalability, and cost-effectiveness of ML workloads.
Develop internal tools and workflows that are user-friendly for both human operators and automated agents.
Collaborate on the architecture that governs the deployment, serving, and operation of models across research and production environments.

