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ML Platform Engineer at Synthesia
About the Role
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Headquartered in London with global offices across Europe and the US, Synthesia builds products to enhance visual communication and enterprise skill development, helping organisations succeed!
Following the recent Series E funding round (RAISED $200M with a $4B valuation), the company remains funded by leaders including Accel, NVentures, Kleiner Perkins, GV, and Evantic Capital, with backing from Stripe, Datadog, Miro, and Webflow founders/operators.
This hands-on IC (Individual Contributor) role in the ML Platform team involves:
"Building and operating systems to train, serve, and deploy generative models reliably—including research infrastructure, production serving, internal tooling, and agent-oriented workflows."
Key Responsibilities
- Design and evolve platform systems for secure, efficient ML workflows, including:
- Model training, evaluation, and production serving.
- Reliability, scalability, and observability across research and product environments.
- Developer experience for human and agentic automation.
- Develop infrastructure and tooling that optimises cost efficiency and operational overhead.
- Improve GPU workload scheduling, monitoring, debugging, and debugging for cloud-based systems.
- Build internal abstractions to streamline ML research-prod transitions.
- Collaborate with researchers and product engineers to refine platform capabilities.
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Ideal Profile
Core Requirements
- Production experience: Operating systems in reliability-first environments.
- Systems mindset: Analysing bottlenecks, failure modes, resource usage, and long-term operability.
- Infrastructure expertise:
- Cloud operations (AWS/Azure), Linux, and automation (Terraform/Pulumi).
- Kubernetes for distributed workloads at scale.
- Backend/tooling proficiency: Strong Python skills, with familiarity in language paradigms used for backend systems.
- Strong judgement on automation vs. human control tradeoffs.
- Ownership-driven: Comfort mitigating ambiguity in technical challenges.
- Pragmatic pragmatist: Focuses on right problem/simple solution (avoids premature abstraction).
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Strong Nice-to-Haves
✅ ML infrastructure: Experience with GPU workloads, model serving, or data pipelines.
✅ Observability:熟悉Datadog, OpenTelemetry, and debugging distributed systems.
✅ ML/DevOps tooling: Knowledge of Terraform, Temporal (workflow orchestration), or GitHub Actions.
✅ Edge cases: Adjusted to operating at the intersection of research vs. production systems.
✅ Performance optimization: Background in scheduling, caching, or resource allocation.
✅ Developer tools: Built lightweight product facing tools (e.g., SDKs, toolkit plugins).
"Bonus points" for experience building LLM-powered tools, automated workflows, or lightweight agentic agent workflows!
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