Senior Machine Learning Engineer in Berlin

Location: Hybrid in Berlin, Germany (2 days in-office)

Start Date: ASAP

Full 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?

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!

• 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.
• Languages: Python (expert), SQL.
• MLOps: MLflow, Weights & Biases, or similar.
• Cloud Infra: AWS or GCP.
• 4+ years of professional experience in Machine Learning Engineering or Applied Data Science.
• Production Focus: Proven track record of taking machine learning models out of Jupyter notebooks and deploying them into scalable, production environments.
• Deep NLP/LLM Expertise: Hands-on experience with modern Natural Language Processing techniques, fine-tuning LLMs, and building RAG (Retrieval-Augmented Generation) systems.
• Engineering Fundamentals: Strong software engineering skills in Python. You write clean, testable, and maintainable code.
• MLOps & Cloud: Solid understanding of containerization (Docker), API development (FastAPI), and deploying models on cloud platforms (AWS/GCP).
• Pragmatic Builder: You prioritize shipping working, impactful solutions to users over endlessly tweaking models for minor academic gains.

You will be a cultural fit if...

• You have a High-Ownership Mindset: You take full responsibility for the lifecycle of your models, from inception to production monitoring.
• You are a Critical Thinker: You can translate ambiguous business requirements into concrete machine learning problems.
• You are Collaborative: You enjoy working in cross-functional pods, bridging the gap between deep technical ML concepts and product goals.
• You bring Enthusiasm: A strong passion for the rapidly evolving AI ecosystem and a drive to continuously learn.
• Competitive base salary + attractive equity package.
• Flexible working model (hybrid with a modern, dog-friendly office in the heart of Berlin).
• High-end tech setup (latest MacBook Pro or Linux machine, plus all necessary peripherals).
• Urban Sports Club membership and public transport subsidy (BVG ticket).
• Generous annual learning and development budget for conferences (e.g., NeurIPS, AI summits) and courses.
• 30 days of paid annual leave.

1st. Intro Call: Background, culture check, and mutual alignment with the internal Talent team (30 mins).

2nd. Technical ML Screen (60 mins): A discussion focused on your past projects, how you scope ML problems, define evaluation metrics, and your approach to MLOps.

• Part 1: Applied Python coding and data manipulation.
• Part 2: System design for deploying an ML model into production (handling latency, scaling, and architecture).

4th. Final Stage: Virtual or in-office meeting with the CTO and Product Leadership to discuss long-term vision, commercial impact, and cultural fit.

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