Algorithm Engineer – Optimization & Scientific ML

Project:
We are looking for an Algorithm Engineer – Optimization & Scientific ML to join an innovative AI-driven biotech platform focused on optimizing complex biological processes through scientific machine learning, mathematical optimization, and mechanistic modeling. The platform combines physics-based and data-driven approaches to help scientists design better experiments, reduce uncertainty, and accelerate the development of biological products.

In this role, you will design and implement hybrid modeling and optimization frameworks, bringing advanced algorithms from research into production. Working closely with scientists, ML engineers, and software engineers, you will solve complex real-world challenges at the intersection of AI, mathematics, and biology.

Cooperation: Long-term engagement, full-time
Position: New role
Timezone: European timezone
Location: Remote
English level: Upper-Intermediate or higher

Responsibilities:

• Design and develop hybrid modeling frameworks combining mechanistic models with modern machine learning techniques.
• Develop optimization algorithms for complex biological systems, including uncertainty estimation, sensitivity analysis, and stochastic simulations.
• Build and improve algorithmic pipelines supporting scientific modeling and optimization workflows.
• Design scalable, production-ready solutions and contribute to system architecture.
• Collaborate closely with scientists and cross-functional engineering teams to translate research into production-grade software.
• Implement robust, maintainable, and well-documented algorithms.
• Contribute to cloud-based data and ML infrastructure supporting the platform.
• Continuously improve model performance, scalability, and reliability.

Requirements:

• 3+ years of experience in Algorithm Engineering, Applied Machine Learning, Optimization, or Data Science.
• MSc or PhD in Computer Science, Applied Mathematics, Electrical Engineering, Chemical/Biochemical Engineering, Data Science, or a related technical field.
• Strong background in Scientific Machine Learning (SciML), Numerical Optimization, Optimal Control, or Mathematical Modeling.
• Experience implementing complex algorithms in production environments.
• Strong Python programming skills for scientific computing and machine learning.
• Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Ability to solve complex mathematical and engineering problems.
• Strong communication skills and the ability to collaborate with multidisciplinary teams.
• Comfortable working in a fast-paced startup environment.

Nice to Have:

• Experience with Physics-Informed Neural Networks (PINNs), Neural ODEs, or other hybrid modeling approaches.
• Hands-on experience with uncertainty quantification, sensitivity analysis, or robust optimization.
• Background in biotechnology, computational biology, bioprocess engineering, or life sciences.
• Experience with AWS or GCP, Docker, CI/CD, and production ML pipelines.
• Familiarity with agentic AI frameworks such as Claude Code, Claude Agents SDK, LangGraph, or similar.
• Experience building scalable scientific computing or optimization platforms.

Benefits from 8allocate:

• Team & Culture: Team events, offsites, and a culture that keeps people connected.
• Learning & Development: Budget for courses, certifications, and conferences.
• Wellbeing: Flexible support in line with company policy, with options to support your physical and mental wellbeing (sport, mental health, or medical insurance).
• Rest & Recovery: Paid vacation and sick leave.