ML Platform Engineer

Stack

Responsibilities

• ▹Design and improve 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 workloads running on GPUs and cloud infrastructure are scheduled, monitored, and debugged
• ▹Develop internal tools, abstractions, and agentic systems that reduce operational overhead
• ▹Drive improvements across observability, automation, reliability, and developer experience
• ▹Collaborate closely with researchers and product engineers to turn pain points into robust platform capabilities

Requirements

• ▹Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability
• ▹A systems mindset: naturally thinking in terms of bottlenecks, failure modes, interfaces, and resource usage
• ▹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 on where automation adds leverage and where human control matters most
• ▹Experience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers
• ▹Comfort working in ambiguous environments and owning open-ended technical problems

Nice to have

• ▹Operating ML infrastructure or model serving systems in production
• ▹Supporting research or data-intensive workloads
• ▹Working with GPU-based or other performance-sensitive infrastructure
• ▹Observability and debugging experience in distributed systems
• ▹Familiarity with Terraform, Datadog, GitHub Actions, or similar tools
• ▹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

Soft skills

About the company

Synthesia is the world's leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017 and headquartered in London, with offices across Europe and the US. Following a $200M Series E round, the company is valued at $4 billion, with total funding exceeding $530 million.