• Design and develop hybrid modeling (e.g., physics-informed neural network and neural-ODEs) and optimization frameworks that integrate biological and physical insights with modern machine learning methods.

• Work on optimization problems, uncertainty estimation, sensitivity analysis, and stochastic simulation methods

• Translate research-grade algorithms into reusable, production-quality Python code, and integrate them into Algocell’s cloud-based platform.

• Define system architecture and design, implement, and maintain production-grade data engineering pipelines supporting Algocell’s modeling and optimization platform.

• Collaborate closely with scientists and engineers to ensure models are interpretable, scalable, and aligned with real experimental workflows.

Qualifications

• 3+ years of industry experience in algorithm development (MUST), applied optimization, or data science roles.

• MSc or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Chemical/Biochemical Engineering, Data Science, or a related field from a top-tier university.

• Strong background in numerical optimization, optimal control, or algorithmic modeling, with experience applying these methods to real systems.

• Demonstrated ability to bring complex algorithms into production, ideally in cloud-based environments.

• Strong communication skills and a collaborative, problem-driven mindset.

• A proactive, startup-oriented attitude.

Nice-to-Haves

• Experience with scientific machine learning, hybrid models, PINNs, Neural ODEs, or similar approaches.

• Hands-on experience with uncertainty quantification, sensitivity analysis, or robust optimization.

• Background or strong interest in biology or bioprocessing.

• Familiarity with cloud infrastructure (AWS/GCP), Docker, CI/CD, and production ML/algorithm pipelines.

Ready to Join Us?

Apply now by sending your CV to careers@algocell.ai.

Help us build the next generation of intelligent bioprocess optimization systems.