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 team of 1,500 Personios is building user-friendly products that delight our 15,000+ customers and their million employees. Ready to make an impact from day one?
Machine Learning Engineer (d/f/m)
This role will be Hybrid, based in our London office 2 days a week.
Role Responsibilities: What you’ll do
Design, develop, and deploy robust machine learning and AI systems for a range of products and use cases, including generative AI.
Integrate ML and AI models into production systems, ensuring scalability, reliability, and maintainability.
Deploy and monitor machine learning models and systems, including CI/CD pipelines, automated testing, monitoring, and model versioning.
Leverage cloud platforms (AWS + Snowflake) and ML infrastructure (, SageMaker, feature stores) for scalable deployment.
Collaborate with cross-functional teams (Product, Customer Experience, and other engineering teams) 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.
3+ 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.
Hands-on experience with ML frameworks (, TensorFlow, PyTorch, Hugging Face).
Experience working with backend teams and deploying end-to-end products
What’s a plus?
Background in data science: comfort with experimentation, A/B testing, and measuring ROI/impact of ML projects (not just accuracy).
Experience with NLP or generative AI techniques.
Familiarity with cloud-based ML infrastructure (AWS, Snowflake, SageMaker, etc.).
Why this role?
Join a recently created AI team focused exclusively on delivering LLM and ML-powered projects with real business impact.
Work in a lean, well-supported environment focusing on real use cases and improving our users experience.
Full ownership of end-to-end ML delivery: from prototype to production.
Exposure to high-impact use cases backed by executive sponsorship - high visibility within the organization to build impactful products.
Why Personio?
Personio is an equal opportunities employer, committed to building an integrative culture where everyone feels welcomed and supported. We embrace uniqueness and understand that our diverse, values-driven culture makes us stronger. We are proud to have an inclusive workplace environment that will foster your development no matter your gender, civil status, family status, sexual orientation, religion, age, disability, education level, or race.
At Personio, we value in-person collaboration while also offering flexibility. This role is office-based, with 2 days per week required in your contracted office location. The remaining days can be worked from home or in the office if you prefer. In addition, you’ll have 20 Flex Days per year to work remotely from other locations.
Aside from our people, culture, and mission, check out some of the other benefits that make Personio a great place to work:
Receive a competitive reward package – reevaluated each year – that includes salary, benefits, and pre-IPO equity
Enjoy 28 days of paid vacation, plus an additional day after 2 and 4 years
Make an impact on the environment and society with 1 (fully paid) Impact Day
Receive generous family leave, child support, mental health support, and sabbatical opportunities
We enjoy gathering for meals, cultural initiatives, and events like local Summer Sessions and year-end celebrations. There's also healthy snacks, drinks, and a weekly catered lunch.
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