Algorithm Engineer – Optimization & SciML

Apply now by sending your CV to [email protected]

Company Description

At Algocell, we apply machine learning, optimization, and mathematical modeling to improve how cells grow and produce proteins and advanced materials. Our platform combines physics-based and mechanistic models with data-driven approaches to help biotech companies design and run better experiments with less guesswork.

If you are motivated by real-world problems involving hybrid models, uncertainty, optimal decision-making, and bringing algorithms into production, this role offers an opportunity to apply deep technical skills beyond the standard ML stack.

Algocell is backed by leading private venture capital funds and the Israeli Innovation Authority. We are based in Rehovot, conveniently located near the train station, and operate in a hybrid work model with three days in the office and two days working remotely.

Role Description

• 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 [email protected].

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