Machine Learning Engineer – GenAI (f/m/d)

About Tomorrow Things

At Tomorrow Things, we're building intelligent infrastructure for industrial support. Our Remote Support Platform uses AI agents, real-time telemetry, and continuous learning to minimize downtime and maximize efficiency for machine operators. We're looking for a Data Scientist / ML Engineer to take this to the next level by driving predictive intelligence, agent learning, and real-time diagnostics.

Your Role

As a Data Scientist / Machine Learning Engineer --- GenAI, you'll design, develop, and deploy models that enable smarter, faster support --- from AI agent optimization to predictive maintenance and multilingual NLP.

What You'll Do

• Develop and refine AI agent models using historical support data and technician feedback
• Build real-time anomaly detection and diagnostics systems using machine telemetry
• Design and maintain supervised and reinforcement learning loops for support automation
• Collaborate with engineering and product teams to integrate models into production
• Analyze support outcomes, SLA metrics, and satisfaction scores to improve support quality
• Contribute to Things OS data pipelines and the broader AI/ML platform strategy

What You Bring

• 3+ years in data science or ML engineering roles, ideally in IoT, SaaS, or support platforms
• Solid Python and ML libraries experience (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience with NLP (preferably multilingual) and time series modeling
• Familiarity with edge/cloud connectivity systems and industrial data is a plus
• Strong communication skills and product-oriented mindset

Why Join Tomorrow Things?

• Apply machine learning to real-world industrial problems
• High impact role in a growing AI-focused team
• Fully remote, flexible work culture with hybrid options in Bonn
• Competitive compensation including stock options
• Work with diverse data, from language to telemetry, and shape future capabilities

👉 Ready to shape the digital factory of the future? Apply directly via LinkedIn.

Let's build tomorrow's factories, today.