Description review
Principal ML Platform Engineer
Synthesia · Europe · back to the listing
HR standards
55/100
needs work
Title ↔ description
75/100
solid
Reads as
Unclear
no confident match
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- 30 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.