About Us
Profluent is a biotechnology software company focused on using modern machine learning to design proteins. In practice, that means building computational systems that can propose new protein sequences with specific properties, helping scientists move from an idea to a candidate molecule more quickly than traditional trial and error approaches. The problem it is tackling is a familiar one in life sciences, experimental work is expensive and slow, and early design choices strongly influence whether a project succeeds. Profluent’s work sits at the intersection of AI and wet lab biology, aiming to make protein engineering more predictable and scalable.
The people who use or benefit from Profluent’s technology are likely to be teams working on therapeutics, diagnostics, and other protein based products, including biotech companies and research organisations. Because protein design is foundational to many areas of drug discovery and synthetic biology, the company’s platform is geared towards users who need high quality candidates and clear evidence for why a design is worth testing. You can expect an emphasis on translating computational outputs into results that are meaningful in the lab, such as improved binding, stability, specificity, or manufacturability, depending on the application.
Within the broader SaaS ecosystem, Profluent looks less like a horizontal business software provider and more like a deep tech, science led platform company. It is effectively building specialised software and data infrastructure for biological design, where the “product” is not just an interface but the combination of models, datasets, evaluation methods, and the workflows that connect computational design to experimental validation. For job seekers, that usually means the company’s success depends on both strong engineering fundamentals and strong scientific judgement, with a need to ship reliable tools while operating in a domain where ground truth comes from experiments.
People who tend to thrive in this kind of environment include machine learning engineers with experience in generative models and scientific ML, software engineers who enjoy building robust data and model platforms, and computational biologists or bioinformaticians who can bridge biology and computation. Depending on the team structure, there is also likely room for product minded roles that can translate researcher needs into usable workflows, as well as lab facing scientists who can design experiments and close the loop between model predictions and real world outcomes. Comfort with ambiguity, careful thinking about validation, and an ability to collaborate across disciplines are usually important in protein design organisations.
What may appeal to candidates is the chance to work on a mission that is tangible and high impact, improving how new biological medicines and tools are created. Profluent’s work also tends to suit people who like technically demanding problems, from building scalable ML systems to making sense of noisy biological data, and who value a culture where engineering and science inform each other. If you are motivated by applied research, rigorous experimentation, and building software that directly influences lab decisions, Profluent is the sort of company where that combination is central to the day to day work.