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.
Role Summary
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).
Domain Learning & Collaboration
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.
Responsibilities
Design, develop, and deploy advanced machine learning models for process modeling and optimization, with a focus on PyTorch v2 and PyTorch LightningImplement and benchmark diverse modeling approaches : physics-informed neural networks (PINNs), hybrid mechanistic-ML models, and complementary frameworksResearch, evaluate, and integrate open-source alternatives to ensure the platform uses the latest best practices in scientific modelingBuild and optimize time-series analysis systems for process monitoring and predictive controlArchitect and implement distributed training and inference using Ray (primary) and other distributed computing frameworksDevelop scalable data pipelines and ETL processes for process data integration and analysisCollaborate closely with modeling engineers and domain experts to translate scientific requirements into ML solutionsImplement MLOps practices using MLflow (primary) and alternative experiment tracking and model versioning toolsBuild comprehensive data validation, profiling, and monitoring systems to ensure data quality and model reliabilityDesign and maintain RESTful and gRPC APIs for ML model serving and real-time inferenceImplement hyperparameter tuning using Ray Tune and other optimization frameworksOptimize models for GPU utilization and distributed computing performanceContribute to statistical analysis, experimental design, and scientific method validation for process modelsWrite maintainable, well-tested code following team quality standards and participate in code reviewsRequirements
Master’s degree or higher in Computer Science, Data Science, Machine Learning, Mathematics, Physics, Chemical Engineering, or a closely related technical field5+ years’ professional experience in machine learning, data science, or scientific computing with production ML systemsExpert-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 productionStrong mathematical and statistical background including optimization algorithms, numerical methods, and statistical modelingStrong experience with the Python data science stack : Pandas, NumPy, Scikit-learn, and Jupyter ecosystemsExperience with distributed computing and scaling ML workloads using Ray (preferred), Spark, or DaskHands-on experience with time-series analysis, regression modeling, and statistical methods for scientific dataExperience with MLOps tools and practices : MLflow (preferred) for experiment tracking, model versioning, and lifecycle managementExperience with hyperparameter tuning and optimization frameworks (Ray Tune preferred, Optuna, or similar)Knowledge of GPU optimization and distributed computing for efficient model training and inferenceProficiency with data validation, profiling, and analysis tools and methodologiesExperience with specialized ML libraries for time-series, regression, and differential equationsExperience designing and operating data pipelines, ETL workflows, and data warehouse solutionsKnowledge of RESTful and gRPC APIs for ML model serving and microservices integrationStrong 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) environmentsKnowledge of cloud platforms (AWS, GCP, or Azure) and containerization with KubernetesStrong Git workflows, CI / CD fundamentals, and Agile / Scrum collaboration experienceExcellent written and verbal communication skills in EnglishNice to have Physics‑informed neural networks (PINNs) & scientific ML depth experienceNice to have Chemical engineering / bioprocessing domain exposureNice to have experience with Process control / MPC (Model Predictive Control), optimization methodsPreferred Qualifications
We welcome applicants who meet most —but not necessarily all—of the preferred qualifications listed below.
Priority levels :
Highly Desirable |DesirableExperience with physics-informed neural networks (PINNs) or scientific machine learningBackground in chemical engineering, bioprocessing, or related scientific domainsExperience with hybrid mechanistic-ML modeling approachesRay distributed computing experience for ML workloadsAdvanced time-series modeling and forecasting techniquesExperience with specialized ML libraries for scientific computing (torchdiffeq, Neural ODEs, …)Experience with process control, MPC (Model Predictive Control), or optimizationProficiency with scientific computing libraries : SciPy, SymPy, or similarUnderstanding of EU AI Act compliance requirements and implementationExperience with Apache ecosystem : Spark, Parquet, Kafka for big data processingKnowledge of statistical process control and design of experiments (DoE)Experience with data visualization : Plotly, Matplotlib, ECharts, or similarMLOps tooling : Kubeflow, BentoML, or model serving platformsExperience with feature stores and analytics platforms : Feast, TectonContributions to open-source scientific computing or ML projectsBackground in applied mathematics, statistics, or engineering physicsFamiliarity with other languages such as C#, Python, or Go for smoother integrationWorking proficiency in German is a plusBenefits
Innovation Culture : We are an international team. We value new ideas, open discussions, and constructive criticism. Your voice shapes our technological directionProfessional Growth : Continuous learning opportunities and career development in cutting-edge softwareMeaningful Impact : Work on software that accelerates life-saving therapies, enzyme manufacturing and sustainable food productionCompetitive 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 higherFull time (38,5 h / week) – 25 days of paid holidays per full calendar year