At a Glance
- Tasks: Design and improve ML platform systems for training and deployment.
- Company: Join Synthesia, the leading AI video platform trusted by Fortune 100 companies.
- Benefits: Competitive salary, health benefits, remote work options, and growth opportunities.
- Other info: Collaborative culture with a focus on innovation and automation.
- Why this job: Shape the future of AI with hands-on impact in a dynamic environment.
- Qualifications: Experience in production systems, cloud infrastructure, and strong coding skills.
The predicted salary is between 80000 - 100000 £ per year.
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.
We’re looking for an 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.
ML Platform Engineer employer: Synthesia
At Synthesia, we pride ourselves on being a forward-thinking employer that champions innovation and collaboration in the heart of London. Our dynamic work culture fosters creativity and growth, offering employees ample opportunities to develop their skills while working on cutting-edge AI technology. With significant backing from top-tier investors and a commitment to employee well-being, we provide a supportive environment where your contributions directly impact the future of visual communication.
StudySmarter Expert Advice🤫
We think this is how you could land ML Platform Engineer
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Synthesia or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Synthesia.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Synthesia.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Synthesia that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace ML Platform Engineer
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Synthesia.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Synthesia and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Synthesia
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Synthesia uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
