> Markdown version of [/jobs/ext/2432857-ml-platform-engineer](https://www.wearedevelopers.com/jobs/ext/2432857-ml-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Platform Engineer - **Company:** Synthesia Limited - **Location:** UK (Remote available) - **Salary:** £78,708.0 - **Contract:** Permanent contract - **Skills:** Cloud Computing, Software Debugging, Linux, Programming Tools, Distributed Systems, Github, Python (Programming Language), Performance Tuning, Azure Machine Learning, Datadog, Graphics Processing Unit (GPU), Large Language Models, Backend, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Software Coding, Terraform - **Published:** August 14, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5841958497 ## About the Role * 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. * 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. * 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. ## Description 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. * 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. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Platform Engineering vs. DevOps Why not both?](https://www.wearedevelopers.com/videos/885-platform-engineering-vs-devops-why-not-both) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)