Front-End Software Engineer (React/Typescript)
About the role
Junior to Mid-Level frontend engineer role at ReactWise in Cambridge (hybrid) focusing on building responsive UI with React and TypeScript for chemical process optimization.
Responsibilities
- Build responsive, user-friendly front-end components using React and TypeScript
- Collaborate with engineers and chemists to translate ideas and wireframes into functional UI
- Contribute to design discussions and input on improving user experience
- Write clean, well-documented code and participate in code review
- Test and debug front-end features across different browsers and screen sizes
Qualifications
- Right to work in the UK (visa sponsorship not available)
- Bachelor's degree in CS, Engineering, or a related field, or equivalent practical experience
- Experience building web applications with React and TypeScript (through work, projects, or coursework). Please link your GitHub and/or portfolio on your CV
- Understanding of HTML, CSS, modern JavaScript/TypeScript, and collaborative Git workflows
- Eagerness to learn and contribute in a fast-paced start-up environment
- Excellent communication skills and the ability to work well in a team
Skills
About ReactWise
AI Co-Pilot for Chemical Process Optimization
Our Mission: We aim to accelerate and automate chemical process development by equipping wet-lab chemists with the power of data-driven optimization and robotic execution of experiments. The Problem: The discovery of novel pharmaceuticals is one of our most important weapons in fighting disease. However, the drug development pipeline is often held up for many months during the design of chemical processes to manufacture these drugs at scale, delaying FDA trials and lengthening the time until drug launch. Designing chemical processes involves the identification of suitable parameters such as catalyst/temperature/solvent. Currently process development is often done via tedious trial-and-error experimentation (slow) or exhaustive screening (expensive and wasteful). Our Approach: In our research, we have developed algorithms for chemical process optimization, which leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to 95% reduction in required experiments and cost compared to exhaustive screening. We have made our approaches accessible to chemists through our user-friendly no-code software platform and to automated laboratory equipment with our API.