ML Engineer, Backend

What you'll do

• Own end-to-end model serving performance, including latency, throughput, batching, memory use, and prediction cost.
• Design, build, and operate secure, scalable backend APIs and cloud infrastructure.
• Implement GDPR-compliant systems and promote engineering practices for testing, CI/CD, documentation, and security.

What they ask for

• Professional experience in machine learning and backend engineering, including managing production infrastructure.
• Experience deploying and operating machine learning models in production.
• Hands-on experience designing and building production-grade APIs and backend services.
• Experience with cloud services, Infrastructure as Code, containerization and orchestration, and proficiency in Python.

Nice to have

• GCP; tools such as Terraform, Docker, and Kubernetes.
• ML infrastructure or inference optimization experience; PyTorch, FastAPI, or relevant open-source contributions.

Tools

GCP, Terraform, Docker, Kubernetes, Python, PyTorch, FastAPI

AI focus

Serve and optimize tabular foundation models in production, including inference performance and deployment across managed APIs, customer VPCs, and self-hosted open weights.