ML Engineer

Personio's intelligent HR platform helps small and medium-sized organizations unlock the power of people by making complicated, time-consuming tasks simple and efficient. Our growing team of 1,800+ Personios across Europe and the US are building user-friendly products that delight our 14,000+ customers and their 1.5 million employees. Ready to make an impact from day one?

ML Engineer - with Data Science Background (d/f/m)
Personio is growing rapidly. With that growth comes the increasing demand for machine learning solutions that don't just provide insights, but power real, production-ready products.

As an ML Engineer, you will play a pivotal role in shaping Personio's AI and ML capabilities. We are looking for someone who can build high-quality ML systems with the rigor of an engineer, while also bringing the curiosity and experimentation mindset of a data scientist. Your work will involve productionizing ML and generative AI models, ensuring they deliver measurable business impact.

You'll join our central AI/ML function, collaborating across departments to drive innovation and enable AI-driven decision-making at scale.

What You'll Do

• Design, develop, and deploy robust machine learning and AI systems for a range of business use cases, including generative AI.
• Build and operationalize ML solutions to address business challenges and unlock new opportunities.
• Integrate ML and AI models into production systems, ensuring scalability, reliability, and maintainability.
• Deploy and monitor MLOps workflows, including CI/CD pipelines, automated testing, monitoring, and model versioning.
• Leverage cloud platforms (AWS + Snowflake) and ML infrastructure (e.g., SageMaker, feature stores) for scalable deployment.
• Collaborate with cross-functional teams (Product, Sales, Marketing, etc.) to deliver AI-driven features and insights.
• Ensure all ML/AI solutions adhere to best practices in data privacy, security, and ethical standards.
• Contribute to a culture of technical excellence, knowledge sharing, and continuous learning.

What You Need To Succeed

• University degree in Computer Science, Machine Learning, Data Science, or a related field.
• 5+ years' experience building and deploying production-grade machine learning models.
• Strong software engineering mindset — ability to write clean, reusable, and scalable code in Python.
• Experience integrating ML/AI models into production software systems.
• Solid understanding of MLOps practices, CI/CD pipelines, and automated testing frameworks.
• Background in data science: comfort with experimentation, A/B testing, and measuring ROI/impact of ML projects (not just accuracy).
• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
• Experience with NLP or generative AI techniques is a strong plus. Familiarity with cloud-based ML infrastructure (AWS, Snowflake, SageMaker, etc.).

Why this role?

• Join a newly created AI sprint team focused exclusively on delivering LLM and ML-powered projects with real business impact.
• Work in a lean, well-supported environment (dedicated TPM, analytics engineer, and data platform support — no need to build ingestion pipelines).
• Full ownership of end-to-end ML delivery: from prototype to production.
• Exposure to high-impact use cases (e.g., automating marketing campaigns, personalization engines) backed by executive sponsorship.
• A chance to bridge the gap between data science and engineering, bringing cutting-edge AI into production at scale.

Why Personio?
We're one of Europe's fastest-growing tech companies, building the leading HR platform for SMEs. With over 15,000 customers, offices across Europe, and strong backing from top-tier investors, Personio is scaling fast — and AI is a critical enabler of that growth.

We're also proud of our culture:

• A diverse, inclusive workplace where your voice matters.
• Competitive compensation, equity, and benefits. 28 days' vacation, plus an additional day after two and four years.
• Flexible, office-led but remote-friendly working with the option for international work weeks.
• Mental health support, family leave, Impact Days, sabbaticals, and regular team events.