Principal ML Platform Engineer

About This Opportunity

We are representing a well-funded AI technology company headquartered in Europe. Our client is scaling their machine learning infrastructure to support generative model development and deployment across research and product teams. They are seeking a Principal ML Platform Engineer to join their ML Platform team and take ownership of critical systems that power model training, serving, and deployment.

This is a hands-on individual contributor role with significant technical ownership. You'll work at the intersection of infrastructure, backend systems, and ML operations—building the platform that enables researchers and product engineers to work efficiently and reliably at scale.

Responsibilities

• Design and improve platform systems supporting 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 manually and through automation
• Architect how models are deployed, served, and operated across research and production environments
• Improve scheduling, monitoring, and debugging of workloads running on GPUs and cloud infrastructure
• Create internal abstractions and agentic systems that reduce operational overhead for technical teams
• Drive improvements across observability, automation, reliability, and developer experience
• Collaborate with researchers and product engineers to understand pain points and translate them into robust platform capabilities
• Contribute to technical direction and make pragmatic architectural tradeoffs as the platform scales

What We're Looking For

Required qualifications:

• Strong experience building or operating production systems with focus on reliability, scalability, and maintainability
• 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 in Python or similar languages used for backend systems and tooling
• Strong judgment around where automation adds leverage versus where human control matters 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
• Pragmatic approach: you solve the right problem well without over-engineering

Particularly valuable 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 qualifications:

• 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

Important note: This is not a pure ML Engineer role. Our client is specifically seeking someone with a strong systems and infrastructure background who has exposure to ML environments, rather than an ML specialist.

How We Work With You

Get A Job.ai is representing this confidential client in their search for exceptional platform engineering talent. Here's our process:

• Apply directly through the Get A Job.ai platform
• Our recruiting team will conduct an initial screening to understand your background and fit
• Qualified candidates will be submitted to our client for consideration
• We'll coordinate all interview scheduling and feedback throughout the process
• Please do not attempt to contact the client directly—all communication should flow through Get A Job.ai

Pay

Compensation details will be discussed during the screening process and will be competitive with senior platform engineering roles at well-funded European technology companies.

Equal Employment Opportunity: Get A Job.ai is committed to creating a diverse and inclusive recruitment process. We welcome applications from candidates of all backgrounds and work with our clients to ensure fair and equitable hiring practices.