Machine learning engineer Jobs in Spreitenbach

Computational Materials Scientist
We’re hiring a Computational Materials Scientist with a strong background in both physics-based simulation and machine learning-driven scientific modeling to build and scale domain-specific simulation and data generation workflows. You’ll work with ML researchers and experimental teams to ensure high-quality data for model training and evaluation.
This role is critical to our ability to generate high-fidelity scientific data, validate predictive models, and bridge computational insights with experimental outcomes.
Key Responsibilities:
Advanced Simulation Development & Scientific Computing
Design, develop, and scale high-throughput computational materials workflows utilizing Density Functional Theory (DFT), Molecular Dynamics (MD), phase-field modeling, and related first-principles simulation methods, including their application to solid-state synthesis processes and phase transformations.
Architect and optimize computational pipelines capable of generating and managing large-scale materials datasets comprising tens of thousands of compounds, structures, and simulation outputs.
Develop novel simulation strategies and workflow automation tools to improve throughput, reproducibility, and scientific rigor.
Scientific Data Generation & Validation
Generate high-quality computational datasets for AI/ML model training, validation, and benchmarking across diverse materials systems.
Establish rigorous validation frameworks to benchmark simulation outputs against experimental measurements and published scientific literature.
Evaluate uncertainty, accuracy, and predictive performance of computational methodologies across multiple materials domains.
Cross-Functional Research Leadership
Partner closely with experimental scientists, materials engineers, and machine learning researchers to align computational predictions with real-world material behavior.
Translate experimental observations into simulation hypotheses and computational models that accelerate research and product development.
Translate experimental and physical insights into data-driven and machine learning-based models for materials discovery and optimization.
Provide scientific leadership on computational methodologies, simulation best practices, and data quality standards across research programs.
Innovation & Technical Excellence
Drive continuous improvements in data quality, coverage, reproducibility, and scalability of scientific workflows.
Contribute to the development of next-generation computational frameworks that integrate physics-based simulation with AI-driven materials discovery.
Stay at the forefront of advances in computational materials science, high-performance computing, and scientific machine learning.
Qualifications:
PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computational Science, or a closely related quantitative discipline (candidates near completion of the PhD may also be considered).
Strong academic background from a top-tier university in core materials science and physics, including quantum mechanics, thermodynamics, and solid-state physics.
Extensive experience developing and deploying advanced computational materials science workflows using DFT, MD, or equivalent atomistic and mesoscale simulation techniques, including applications to solid-state synthesis, thermodynamic analysis, and phase transformations.
Demonstrated expertise in high-throughput simulation of large materials libraries, including datasets containing 10,000+ materials, structures, or computational experiments combined with machine-learning-based force fields or related hybrid modeling approaches
Proven track record of validating computational predictions against experimental data and translating simulation results into actionable scientific insights.
Proven ability to integrate physics-based modeling with data-driven or machine learning approaches, including experience in synthetic data generation or advanced AI methods applied to scientific workflows.
Demonstrated combination of deep materials science expertise with formal academic training or graduate-level coursework in machine learning, computer science, or related quantitative fields.
Experience working with large-scale scientific datasets and computational workflows.
Strong experience working in interdisciplinary environments involving experimental researchers, computational scientists, and machine learning teams.
Proficiency with scientific computing, programming skills ( required), workflow orchestration, high-performance computing environments, and large-scale data analysis.
Excellent written and verbal communication skills in English.
Preferred:
Exposure to state of the art machine learning, including reinforcement learning or large language models
Why Join Us:
Work alongside world-class researchers and engineers tackling frontier challenges in materials discovery and scientific AI.
Lead mission-critical computational research that directly influences breakthrough technologies and products.
Access cutting-edge computational infrastructure and collaborative multidisciplinary research environments.
Competitive compensation, comprehensive benefits, and flexible working arrangements.
Opportunity to make a visible and lasting impact on the future of materials innovation jid10348a9a jit0731a jiy26a