Principal ML Platform Engineer
At a glance
Senior ML Platform & Ops role at Synthesia. Remote, United Kingdom · full-time · no salary stated · we estimate £96,000–£116,772 across the United Kingdom.
Synthesia has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Design platform systems for model training, evaluation and production serving across research and product environments
- Build infrastructure that improves ML workload reliability, scalability and cost efficiency
- Create tools and workflows that researchers and engineers can operate, including through agents
- Improve GPU and cloud workload scheduling, monitoring and debugging
- Set technical direction while strengthening observability, automation and developer experience
What you bring
- You have built or operated production systems with strong attention to reliability, scale and maintainability
- You bring hands-on experience with cloud infrastructure, Linux, infrastructure automation and Kubernetes
- You can write strong backend and tooling code in Python or a similar language
- You have built internal platforms, developer tools or infrastructure abstractions used by other engineers
Who this fits
This suits a senior, hands-on individual contributor who can take ownership of open-ended platform problems and make practical architecture choices. You’ll work across infrastructure, backend systems and tooling, collaborating with researchers and product engineers. The role is remote and based in London.
From the employer
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.
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.
Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.
We’re looking for a Principal 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. A growing part of our mission is making these systems more automation-friendly and agent-oriented, so that workflows can increasingly be operated through reliable tooling rather than manual effort.
We’re looking for a strong generalist with a systems mindset:
someone who is comfortable working across infrastructure, backend systems, and tooling, and who has seen ML systems in practice.
this is not a pure ML Engineer role. We’re especially interested in people who think deeply about reliability, scalability, performance, and resource efficiency in complex production environments.
This is a hands-on IC role with significant ownership. You’ll help shape how our ML platform evolves as we scale the number of models, workloads, tools and teams relying on it.
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.
Particularly 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.
Bonus points for
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.