OO1380 – Machine Learning Engineering – IC – F

ML Platform Engineer
Amsterdam, Netherlands (Hybrid)
Long-term contract | 12 months

We’re looking for an experienced ML Platform Engineer with a strong software engineering background to help build and scale a high-performance ML inference platform used across a global technology organisation.
This role sits within the ML Production team and focuses on designing, operating and optimising large-scale ML serving infrastructure powering production machine learning workloads.
What you’ll be doing

• Build and operate scalable ML inference and backend services using Java/Scala and JVM technologies
• Design low-latency, high-throughput distributed systems for online and offline predictions
• Optimize CPU/GPU performance, memory usage and inference workloads
• Work extensively with Kubernetes (AWS EKS/on-prem), containerization and cloud-native deployments
• Develop MLOps capabilities including model deployment, monitoring, rollout strategies and CI/CD pipelines
• Improve observability using Grafana, Graphite, logging and alerting solutions
• Support batch jobs, async workloads and production ML lifecycle management
• Collaborate closely with ML Engineers, Data Scientists and Platform teams

What we’re looking for:
5+ years in ML Engineering / Backend Engineering / Platform Engineering
Strong Java and/or Scala experience
Experience with Kubernetes & AWS EKS
Strong distributed systems and microservices background
Experience with MLOps and ML serving infrastructure
Observability & monitoring expertise (Grafana, Graphite, Prometheus etc.)
Linux and cloud-native engineering experience
Strong performance tuning and scalability mindset

Advantageous to have:
ML serving platforms (SageMaker, Triton, TorchServe, ONNX, Ray etc.)
Spark or distributed compute experience
SRE or Platform Engineering background
Hybrid cloud or bare-metal infrastructure experience
Model observability / ML monitoring experience

If you enjoy building production-grade ML infrastructure at scale and working at the intersection of distributed systems, cloud engineering and machine learning, feel free to connect or message me directly.