Who are we?

Join us at the forefront of innovation in the AI sector. Our mission is to accelerate the future of work. We're not merely envisioning the future; we're actively constructing it. Our work is inspired by Ford's assembly line and Ohno's production system.

We are a well-funded Silicon Valley based Series A startup backed by top-tier VCs.

Our founding team boasts a remarkable track record in AI and the startup ecosystem, with each member having previously steered AI startups to unicorn status. As we write this new chapter in AI, we invite you to be part of this exciting journey. Be a part of an exceptional team that's not just watching the future unfold but actively making a huge impact in a short amount of time.

We are growing our engineering team centered around Berlin and San Francisco.

What you will do

• Model Development: Design, implement, test, and operationalize machine learning systems that uncover causality, optimize decisions, and forecast outcomes.
• Experimentation: Build statistical frameworks and quasi-experimental designs to quantify causal impact.
• Data Analysis & Feature Engineering: Work with large-scale behavioral and operational datasets to extract signal, engineer robust features, and generate actionable insights.
• Deployment: Partner with engineering to deploy ML pipelines that are scalable, maintainable, and observable in production.
• Collaboration: Work with product and business stakeholders to translate ambiguous questions into measurable hypotheses and deploy models that directly influence business outcomes.
• Innovation: Extend classical econometric approaches with modern ML — for example, integrating gradient boosting, deep temporal models, or probabilistic programming.
• Automation & Efficiency: Enhance reproducibility, code efficiency, and team velocity through automation and rigorous software engineering practices.

What we look for

• Advanced degree (Master’s or PhD) in Data Science, Computer Science, Statistics, Econometrics, or a related technical field.
• Minimum 3 years of professional experience in data science, with a strong emphasis on applied machine learning, econometrics, or statistical modeling.
• Expertise in causal inference, experimental design, hierarchical Bayesian modeling, and time series analysis.
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, statsmodels, PyMC, or equivalent).
• A first-principles, data-driven mindset; hands-on and comfortable working with complex, high-dimensional data.
• Excellent communication skills and the ability to translate technical insights into strategic impact.
• Experience with or interest in integrating LLMs into ML, probabilistic modeling, or data science workflows.
• Familiarity with—or willingness to learn—MLOps and data pipeline orchestration in cloud environments (AWS, GCP, or Azure).