Senior Machine Learning Engineer (Global ML Platform)

TL;DR

Senior Machine Learning Engineer (Global ML Platform): Build and evolve centralized platform empowering Data Science and ML teams to develop, deploy, and manage personalized ML models at scale with an accent on model development, training, serving, and monitoring infrastructure. Focus on defining architectural vision, driving complex projects end-to-end, and implementing scalable, resilient systems using public cloud and IaC.

Location: Hybrid in Berlin, Germany (2 days a week in Berlin campus)

Company

World’s pioneering local delivery platform operating in 65+ countries, headquartered in Berlin, Germany.

What you will do

• Define long-term technical vision, roadmap, and architecture for Global ML Platform components managing full ML lifecycle.
• Lead design, build, and maintenance of scalable ML infrastructure services like model training, serving, and monitoring.
• Drive end-to-end complex projects, translating cross-functional needs into high-impact platform features and innovations.
• Implement highly available, secure systems with GCP/AWS, Kubernetes, Terraform, and GitOps practices for global scale.
• Mentor engineers, enforce best practices through docs, RFCs, and code reviews to ensure technical excellence.

Requirements

• Exceptional written and verbal English communication for architectural reviews and cross-team influence.
• 5+ years in Software/ML/Platform Engineering with high-quality code in Python or Golang.
• Hands-on experience designing complex distributed systems, microservices, MLOps, DevOps.
• Mastery of Docker, Kubernetes, Helm, CI/CD, Terraform, public clouds (GCP, AWS).
• Deep knowledge of ML ecosystem: model serving (Triton), training, feature pipelines, tools like MLflow, Metaflow, Argo.
• Expert problem-solving, debugging, RCA in large-scale systems.

Nice to have

• Experience with Jupyter Notebooks.

Culture & Benefits

• Hybrid model with 2 days/week in Berlin campus for collaboration.
• 27 days holiday + extra days after 2-3 years; educational budget €1,000, Udemy, language courses.
• Health perks: checkups, yoga, gym subsidy, meal vouchers, public transport discount.
• Financial: share purchase, sabbatical bank, life insurance, corporate pension.
• Diversity-focused with accommodations for interviews; preferential for severely disabled applicants.