About this role.
Synthesia seeks a Principal ML Platform Engineer to build and operate infrastructure supporting generative-model training, evaluation, deployment, and production serving. This hands-on individual-contributor role emphasizes reliable distributed systems, GPU and cloud workload operations, Kubernetes, automation, observability, and developer tooling. The engineer will partner with researchers and product teams to reduce operational burden and turn platform pain points into robust capabilities. The role requires strong architectural judgment, ownership of ambiguous problems, and pragmatic decisions about scalability, performance, and resource efficiency.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Sample interview questions
I would begin by profiling each model's resource envelope and traffic characteristics, then define workload classes with separate autoscaling and scheduling policies. I would use metrics such as queue depth, latency percentiles, GPU utilization, memory pressure, and error rate to drive placement and capacity decisions. The platform would include safe rollout controls, model versioning, isolation boundaries, and observability that connects requests to infrastructure behavior.
I would first make the failure reproducible or narrow its conditions through correlated logs, traces, job metadata, node events, and GPU telemetry. I would examine common cross-layer causes such as resource contention, networking, storage throughput, driver or CUDA compatibility, orchestration retries, and application-level synchronization issues. After identifying the cause, I would implement a durable mitigation, add targeted alerts and diagnostics, and document operational guidance to reduce recurrence.
I automate work when the action is repeatable, observable, reversible, and has well-defined guardrails. For high-impact or ambiguous actions, I prefer staged automation that recommends or prepares changes while requiring approval until confidence and safeguards are proven. I also ensure every automated action has auditability, clear ownership, rollback paths, and measurable success criteria.
I would identify friction through interviews and workflow telemetry, then provide opinionated self-service interfaces for packaging, validation, deployment, evaluation, and rollback. The default path should encode security, resource requests, observability, and release standards without forcing researchers to become infrastructure specialists. I would measure adoption, time to deploy, deployment failures, and support burden to validate that the platform is improving outcomes.
I would offer standardized, well-supported deployment paths for the majority of workloads while allowing carefully bounded escape hatches for exceptional needs. Broad flexibility can create unmaintainable operational variance, so custom configurations should have explicit ownership, validation, and lifecycle expectations. This approach preserves innovation while keeping the core platform predictable, observable, and economical to operate.
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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