Location: Hybrid in Berlin, Germany (2 days in-office)Salary: €90,000 - €140,000 + EquityLanguage: English speakingStart Date: ASAPFull time: 40 hours per week

🌟 The Company

Do you want to turn cutting-edge AI research into real-world applications that automate complex enterprise workflows? Then apply today!

Our client is a fast-growing, Berlin-based Applied AI scale-up. They are building an intelligent orchestration platform that uses Natural Language Processing (NLP) and Large Language Models (LLMs) to automate heavy compliance, legal, and financial data processing for global enterprises.

Backed by top-tier European venture capital, they are a pragmatic, product-driven team. This is not a pure research lab—this is a high-velocity environment where your models will actually ship to production and solve massive operational bottlenecks for real users.

💡 The Project

As a Senior Machine Learning Engineer, you will bridge the gap between data science and software engineering. You will own the end-to-end lifecycle of machine learning models—from data preparation and fine-tuning to deployment, monitoring, and scaling in a live production environment.

You will collaborate closely with Backend Engineers, Data Engineers, and Product Managers to build robust ML pipelines, optimize model inference latency, and deploy agentic AI systems that interact directly with the company's core SaaS platform.

If you are an engineer who cares just as much about scalable system architecture as you do about model accuracy, this is the role for you!

🎯 Responsibilities

• Model Development: Design, train, and fine-tune NLP models and LLMs to solve specific domain challenges (e.g., document extraction, classification, and semantic search).
• Productionize ML: Build and maintain scalable ML pipelines for continuous training, evaluation, and deployment (MLOps).
• Optimize Inference: Ensure models run efficiently in production, optimizing for low latency, high throughput, and cost-effectiveness.
• Cross-Functional Collaboration: Work hand-in-hand with backend engineers to integrate machine learning models seamlessly into the product via robust APIs.
• Monitor & Iterate: Implement tracking and monitoring for model drift and performance degradation in real-world usage.