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About this job
Key facts
Job Title
ML Platform Engineer - Inference & Production
Role type
Contract
Start date
ASAP
Remote friendly
Yes
Location
Amsterdam, North Holland, Netherlands
Salary
Negotiable €
Machine Learning Engineer - ML Production / Inference Platform
About the role
As a Machine Learning Engineer within a central ML Production team, you will design, build and operate the core backend services that power a company‑wide ML inference platform. This platform is used by many product and ML teams to run reliable, high‑performance online and batch predictions at scale.
You will primarily work on JVM‑based services, optimising core ML inference capabilities. The environment is cloud‑native and hybrid, running on Kubernetes (on‑prem and managed cloud) with a strong focus on performance, reliability and observability. A key part of the role is ensuring the platform is efficient, resilient and easy for other teams to integrate with.
You will collaborate closely with ML engineers, data scientists and platform teams to run and optimise inference workloads, evolve the infrastructure and drive best practices in MLOps and model lifecycle management.
Key responsibilities
- Design, implement and operate high‑throughput, low‑latency ML serving services using Java, Scala or other JVM‑based languages
- Profile and optimise CPU, GPU and memory usage for inference services; run benchmarks, load tests and capacity experiments to control latency and cost
- Build and maintain distributed systems for online and offline predictions, including APIs, async/batch jobs and client libraries
- Develop and operate services on Kubernetes, including containerisation, deployment pipelines, autoscaling and rollout strategies
- Own and improve scheduled and cron‑based workloads such as batch predictions, maintenance tasks and data migrations
- Implement robust observability using metrics, logging, alerting and dashboards for inference services and platform components
- Contribute to MLOps practices, including model versioning, canary and shadow deployments, traffic shifting, health checks, automated testing and CI/CD
- Participate in on‑call rotations and incident response, perform root‑cause analysis and drive long‑term reliability improvements
- Work with ML practitioners and product teams to translate use cases into scalable serving solutions and guide best platform usage
- Contribute to technical design documents, runbooks and standards, and share knowledge through reviews, mentoring and internal talks
Required qualifications
- Solid professional experience (typically 5+ years) as a Machine Learning Engineer or Software Engineer building and operating production systems
- Strong experience with Java and/or Scala (or another JVM language), including concurrency and performance tuning
- Proven background in distributed systems (e.g. microservices, RPC, caching, queues, streaming) and designing for scalability and reliability
- Good understanding of CPU/GPU/memory constraints, profiling and performance benchmarking of server‑side applications
- Hands‑on experience running services on Kubernetes (preferably managed Kubernetes in cloud environments)
- Experience with scheduled workloads such as batch processing, housekeeping jobs or data pipelines in production
- Practical experience with observability tooling, including metrics, dashboards and alerting
- Familiarity with MLOps concepts such as model deployment, monitoring, experimentation and CI/CD for ML services
- Comfortable working in Linux‑based environments and with common cloud primitives (networking, load balancers, IAM, storage)
- Strong communication skills and the ability to collaborate effectively with ML, engineering and product stakeholders
- A continuous‑improvement mindset, proactively improving tooling, automation, processes and workflows
Let op: vacaturefraude
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* Wij zullen nooit via WhatsApp of in een videogesprek vragen om jouw persoonlijke gegevens (zoals een kopie van je ID, bankgegevens of BSN).
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Important: job fraud
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* We will never ask for personal information (such as a copy of your ID, bank details, or social security number) via WhatsApp or during a video call.
* If you're unsure whether a vacancy or contact person is legitimate, please reach out to us directly using the official contact details on our website.
