Applied Machine Learning Engineer, Industry Solutions

Terra Quantum seeks an Applied Machine Learning Engineer in Geneva to build hybrid classical-quantum ML solutions for industrial clients, focusing on end-to-end pipeline design and rigorous experimentation.

This position sits within the AI Applied Research team at Terra Quantum, where the primary objective is to develop complete machine learning systems for industrial partners. The role focuses on crafting hybrid solutions that integrate classical machine learning techniques with potential quantum computing layers, ensuring these systems are robust, scalable, and deliver measurable value to clients.

Responsibilities

• Architect end-to-end machine learning pipelines tailored to specific client challenges, including areas like time series analysis, routing, planning, generative AI, natural language processing, computer vision, and predictive modeling.
• Choose classical machine learning algorithms based on the specific characteristics of the data rather than adhering to a preferred framework.
• Integrate quantum components as constrained elements within hybrid pipelines, applying classical ML best practices to ensure stability and functionality.
• Execute data preparation tasks such as cleaning, preventing data leakage, designing cross-validation strategies, establishing baselines, and conducting statistical significance tests.
• Create feature representations that are compatible with quantum components, including the development of Fourier-spectrum features.
• Analyze and enhance training stability, particularly in scenarios involving noisy or non-standard gradients.
• Develop and maintain internal machine learning libraries and SDKs to promote code reuse across different client projects.
• Convert quantum machine learning algorithms proposed by the research team into practical, testable implementations.
• Assess the specific conditions under which adding a quantum layer provides a tangible performance advantage in applied tasks.

Requirements

• A Master's degree in computer science, applied mathematics, data science, statistics, engineering, physics, or a closely related field.
• Practical experience with classical machine learning gained through academic coursework, internships, research projects, or early-career roles.
• Strong proficiency in Python, NumPy, pandas, scikit-learn, and at least one major deep learning framework such as PyTorch or TensorFlow.
• Solid understanding of tree-based methods (e.g., XGBoost, LightGBM, random forests), gradient boosting, and kernel methods.
• Proven ability to design rigorous experiments, including cross-validation, baseline creation, statistical testing, and hold-out evaluation.
• Strong software engineering fundamentals, including version control with Git, testing, reproducible environments, and configuration-driven experiments.
• Fluency in written and spoken English.
• Legal authorization to live and work in the European Union or Switzerland.

Nice to have

• Prior experience with quantum computing frameworks such as PennyLane, Qiskit, or Cirq.
• Familiarity with specific applied machine learning domains like time series forecasting, natural language processing, computer vision, or industrial optimization.

What the company offers

• A flexible work model allowing for significant remote work or office-based collaboration.
• The chance to collaborate with talented experts in quantum technologies and experienced leadership.
• Access to cutting-edge developments in science and engineering.
• A personal development plan with defined goals for career advancement.
• A competitive salary package.
• Flexible working arrangements to support work-life balance.

About the company

Terra Quantum fosters a vibrant and enthusiastic team culture driven by trust, excellence, and a commitment to continuous improvement. The environment is diverse and supportive, encouraging innovation and individual initiative among its employees.

• A team culture characterized by enthusiasm, passion, and creativity.
• An atmosphere built on trust, excellence, and continuous improvement.
• A diverse and supportive environment that encourages innovation and personal initiative.