At Novasign, we're redefining the future of bioprocessing. Our platform, Novasign Studio, combines intelligent hybrid models, automation, and modern microservices (SOA) architecture to accelerate the development of life-saving therapies, next-generation enzymes, and sustainable food technologies.

We're scaling fast with a growing customer base across biotech and life sciences. If you're looking to make a real-world impact with cutting-edge machine learning and SaaS technology, we want to hear from you. We're hiring ten new team members in the next four months, and this role is critical in driving our growth.

We are seeking a Senior ML Engineer - Data Science & Scientific Modeling with strong expertise in data science and scientific modeling to design and implement advanced machine learning solutions for the optimization of complex scientific and industrial processes. You will develop and benchmark diverse modeling approaches, including physics‑informed neural networks, hybrid mechanistic‑ML models, and complementary frameworks, to ensure our platform incorporates the latest best practices in scientific modeling. This role combines cutting‑edge ML techniques with domain learning—using PyTorch and distributed computing—to create predictive models that accelerate development and operations across various domains (bioprocessing experience is a big plus).

What makes this role unique is the opportunity to work at the intersection of cutting-edge ML/AI and bioprocessing. While prior bioprocessing knowledge is not required (though it is nice to have), you should be:

• Enthusiastic about learning complex bioprocessing concepts from our expert consulting team
• Ready to engage in deep technical discussions with process engineers and scientists
• Excited to bridge the gap between biotech domain experts and software solutions
• Able to translate between technical requirements and domain-specific challenges
• Comfortable representing software engineering best practices and conventions to the biotech team
• Ready to collaboratively prototype solutions with the consulting team to validate approaches

In this role, you'll serve as a key bridge between our software development and biotech consulting teams, helping establish effective collaboration patterns and ensuring solutions meet both technical and domain requirements.

• Design, develop, and deploy advanced machine learning models for process modeling and optimization, with a focus on PyTorch v2 and PyTorch Lightning
• Implement and benchmark diverse modeling approaches: physics-informed neural networks (PINNs), hybrid mechanistic-ML models, and complementary frameworks
• Research, evaluate, and integrate open-source alternatives to ensure the platform uses the latest best practices in scientific modeling
• Build and optimize time-series analysis systems for process monitoring and predictive control
• Architect and implement distributed training and inference using Ray (primary) and other distributed computing frameworks
• Develop scalable data pipelines and ETL processes for process data integration and analysis
• Collaborate closely with modeling engineers and domain experts to translate scientific requirements into ML solutions
• Implement MLOps practices using MLflow (primary) and alternative experiment tracking and model versioning tools
• Build comprehensive data validation, profiling, and monitoring systems to ensure data quality and model reliability
• Design and maintain RESTful and gRPC APIs for ML model serving and real-time inference
• Implement hyperparameter tuning using Ray Tune and other optimization frameworks
• Optimize models for GPU utilization and distributed computing performance
• Contribute to statistical analysis, experimental design, and scientific method validation for process models
• Write maintainable, well-tested code following team quality standards and participate in code reviews

• Master's degree or higher in Computer Science, Data Science, Machine Learning, Mathematics, Physics, Chemical Engineering, or a closely related technical field
• 5+ years' professional experience in machine learning, data science, or scientific computing with production ML systems
• Expert-level Python programming skills (core constructs, modules, packaging: UV, Poetry, pip)
• Deep expertise in PyTorch v2 and PyTorch Lightning for building, training, and deploying ML models in production
• Strong mathematical and statistical background including optimization algorithms, numerical methods, and statistical modeling
• Strong experience with the Python data science stack: Pandas, NumPy, Scikit-learn, and Jupyter ecosystems
• Experience with distributed computing and scaling ML workloads using Ray (preferred), Spark, or Dask
• Hands-on experience with time-series analysis, regression modeling, and statistical methods for scientific data
• Experience with MLOps tools and practices: MLflow (preferred) for experiment tracking, model versioning, and lifecycle management
• Experience with hyperparameter tuning and optimization frameworks (Ray Tune preferred, Optuna, or similar)
• Knowledge of GPU optimization and distributed computing for efficient model training and inference
• Proficiency with data validation, profiling, and analysis tools and methodologies
• Experience with specialized ML libraries for time-series, regression, and differential equations
• Experience designing and operating data pipelines, ETL workflows, and data warehouse solutions
• Knowledge of RESTful and gRPC APIs for ML model serving and microservices integration
• Strong grasp of key design patterns and practices (e.g., DDD, SOLID, DRY, KISS, Composition, Inheritance)
• Experience with Docker & Docker Compose, and comfortable developing on Ubuntu (WSL2) environments
• Knowledge of cloud platforms (AWS, GCP, or Azure) and containerization with Kubernetes
• Strong Git workflows, CI/CD fundamentals, and Agile/Scrum collaboration experience
• Excellent written and verbal communication skills in English
• Nice to have Physics‑informed neural networks (PINNs) & scientific ML depth experience
• Nice to have Chemical engineering / bioprocessing domain exposure
• Nice to have experience with Process control / MPC (Model Predictive Control), optimization methods

We welcome applicants who meet most —but not necessarily all—of the preferred qualifications listed below.

Priority levels: ●● Highly Desirable | ● Desirable

• Experience with physics-informed neural networks (PINNs) or scientific machine learning ●●
• Background in chemical engineering, bioprocessing, or related scientific domains ●●
• Experience with hybrid mechanistic-ML modeling approaches ●●
• Ray distributed computing experience for ML workloads ●●
• Advanced time-series modeling and forecasting techniques ●●
• Experience with specialized ML libraries for scientific computing (torchdiffeq, Neural ODEs, ...) ●●
• Experience with process control, MPC (Model Predictive Control), or optimization ●
• Proficiency with scientific computing libraries: SciPy, SymPy, or similar ●●
• Understanding of EU AI Act compliance requirements and implementation ●●
• Experience with Apache ecosystem: Spark, Parquet, Kafka for big data processing ●
• Knowledge of statistical process control and design of experiments (DoE) ●
• Experience with data visualization: Plotly, Matplotlib, ECharts, or similar ●
• MLOps tooling: Kubeflow, BentoML, or model serving platforms ●
• Experience with feature stores and analytics platforms: Feast, Tecton ●
• Contributions to open-source scientific computing or ML projects ●
• Background in applied mathematics, statistics, or engineering physics ●
• Familiarity with other languages such as C#, Python, or Go for smoother integration ●
• Working proficiency in German is a plus

• Innovation Culture: We are an international team. We value new ideas, open discussions, and constructive criticism. Your voice shapes our technological direction
• Professional Growth: Continuous learning opportunities and career development in cutting-edge software
• Meaningful Impact: Work on software that accelerates life-saving therapies, enzyme manufacturing and sustainable food production
• Competitive Package: We offer an attractive salary above industry standards, complemented by comprehensive benefits, including a free food allowance. In accordance with the IT collective agreement (minimum ST1 - Regelstufe), the minimum gross annual salary is €53,802; however, your actual compensation will reflect your skills, experience, and impact and will be significantly higher
• Full time (38,5 h/week) - 25 days of paid holidays per full calendar year