Senior Machine Learning & Data Science Engineer (Python)

About Us

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 Machine Learning & Data Science Engineer with a strong Python background to design and implement advanced ML models, scalable data pipelines, and analytics infrastructure for Novasign Studio. You will drive the development, deployment, and monitoring of ML solutions (with a focus on PyTorch and distributed training), and play a key role in shaping our data-driven product features. This is a hands-on role for someone who thrives at the intersection of ML/AI, data engineering, and robust backend systems, and who is passionate about building production-grade ML workflows and collaborating with domain experts in bioprocessing.

Domain Learning & Collaboration

What makes this role unique is the opportunity to work at the intersection of cutting-edge ML/AI and bioprocessing. While no prior bioprocessing knowledge is required, 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
• Willing to ask questions and continuously learn about the problems we’re solving
• 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.

Key Responsibilities

• Design, develop, and deploy machine learning models (with a focus on PyTorch, but also scikit-learn, and other modern deep learning frameworks)
• Fully understand and apply best practices like Modular Design, OOP, SOLID, DRY, KISS, Composition, and Inheritance
• Build, optimize, and maintain scalable data pipelines, ETL processes, and analytics services for ML and data science workflows
• Architect and implement distributed training and inference using Ray (primary), Spark, or Dask for large-scale ML workloads
• Drive the adoption of best practices in ML engineering: experiment tracking (MLflow), model versioning, reproducibility, and robust testing
• Collaborate with data scientists, domain experts, and backend engineers to translate business and scientific requirements into production ML solutions
• Develop and maintain data validation, profiling, and monitoring systems to ensure data quality and model health (Great Expectations, ydata-profiling, custom dashboards)
• Contribute to the design and operation of data warehouses, feature stores, and data modeling for ML applications
• Implement and document RESTful and gRPC APIs for ML model serving and data access
• Participate in code reviews, pair programming, and knowledge-sharing sessions to uphold engineering excellence
• Ensure observability and monitoring of ML systems (metrics, logging, tracing)
• Stay current with advances in ML, MLOps, and data engineering, and help drive continuous improvement in our ML stack
• Write maintainable, well-tested code following our team’s quality standards, participating actively in code reviews and knowledge sharing sessions

Required Qualifications

• Master’s degree in Computer Science, Data Science, Machine Learning, or a closely related technical field
• 5+ years’ professional experience in machine learning, data science, or applied ML engineering
• Deep expertise in Python for ML and data science (core constructs, modules, packaging: UV, Poetry, pip)
• Strong experience building, training, and deploying ML models in production (PyTorch required; scikit-learn, TensorFlow, or JAX a plus)
• Experience with distributed training, parallelization, and scaling ML workloads (Ray, Spark, or Dask); direct ML experience with Ray is a major plus
• Hands-on experience with the Python data stack: Pandas, NumPy, PySpark, JupyterLab
• Experience designing and operating data pipelines, ETL workflows, and data-warehouse solutions; solid understanding of data modeling and statistical analysis
• Experience with experiment tracking, model versioning, and MLOps tools (MLflow, DVC, etc.)
• Experience with data validation and profiling (Great Expectations, ydata-profiling, or similar)
• Familiarity with feature stores, data warehousing, and analytics for ML
• 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
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Kubernetes)
• Experience with RESTful and gRPC APIs for ML model serving and data access
• Strong Git workflows, CI/CD fundamentals, and Agile/Scrum collaboration
• Excellent written and verbal communication skills in English

Preferred Qualifications

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

• Priority levels: ●● Highly Desirable | ● Desirable
• Deep learning frameworks: PyTorch Lightning ●●
• Data visualization libraries: Matplotlib, Seaborn, Plotly, Dash ●●
• Data profiling and validation tools: Pandas Profiling ●
• Workflow orchestration: Airflow, Prefect ●
• Big-data and streaming technologies: Kafka ●
• MLOps tooling: Kubeflow, BentoML, Seldon Core ●●
• Feature stores and analytics platforms: Feast, Tecton, Superset ●
• Background in statistics, probability, or applied mathematics ●●
• Contributions to open-source projects or tech-community involvement ●
• Familiarity with SQL and experience working with relational databases (PostgreSQL, MySQL) ●
• Basic front-end skills in HTML, CSS, and JavaScript for collaborating with frontend teams ●
• Working proficiency in German is a plus

What We Offer

• 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

How to Apply

Please apply directly via our LinkedIn job posting (preferred). This route ensures your application is tracked correctly and reaches the hiring team the fastest.

If applying through LinkedIn is not possible, you may instead email us at [email protected] with the subject line “Senior Machine Learning & Data Science Engineer (Python)” and include your CV, GitHub (or comparable) profile, and a short cover letter.

We review applications on a rolling basis and aim to respond within one week.