Solutions Consultant, Machine Learning

This is Cradle Proteins are the molecular machines of life, used for many therapeutic, diagnostic, chemical, agricultural and food applications. Designing and optimizing proteins takes a lot of expert knowledge and manual effort, through the use of custom computational and biological tools. Machine learning is revolutionizing this space, by enabling high-fidelity protein models. At Cradle, we offer a software platform for AI-guided discovery and optimization of proteins, so that biologists can design proteins faster and at scale. We are already used by clients across biopharma, biotech, agri-tech, food-tech, and academia. We're an experienced team of around 90 people. We've built many successful products before and have enough funding for multiple years of runway. We are distributed across two main locations, Zurich and Amsterdam, and are focused on building the best possible team culture. We offer our employees a very competitive salary, a generous equity stake in the company and a wide range of benefits and career progression opportunities. The Role As a ML-focussed Solutions Consultant on our Commercial team, you are the person who speaks fluently with the computational teams at biotech and pharma companies that are building serious in‑house ML capabilities. You'll partner with Account Executives across the full buying journey, owning the technical narrative with ML engineers, computational biologists, and research leaders who want to understand not just what Cradle does, but how it works and why it’s better than other platforms. You'll bring genuine ML depth to every conversation, and feed what you learn back to the teams building the product. Your Impact Own the technical discovery process with prospects — understanding their computational stack, ML workflows, and protein engineering objectives to position Cradle where it creates the most value Build and deliver compelling technical demonstrations that speak directly to ML‑savvy audiences, including engineering and research leadership Drive the conversation with computational and ML teams at prospects, handling deep technical questions with authority and earning credibility fast Shape how Cradle tells its ML story externally — developing presentations, demos, and assets that set a new bar for technical sales at the company Feed insights from the field into the product and ML teams, influencing roadmap decisions with ground‑truth intelligence from the market Partner with and mentor other SSCs, helping the team grow its technical depth over time Your Expertise Great candidates will have experience with most of the following areas, while being eager to develop in others: A graduate degree (MS or PhD) in machine learning, computational biology, computer science, or a closely related field Hands‑on ML engineering experience — you’ve trained models, worked with sequence or structure data, and understand the stack from data pipeline to deployment Familiarity with protein engineering or structural biology contexts — you don’t need to have run a wet lab, but you understand the problems these scientists are trying to solve Ability to translate technical depth into clear, persuasive narratives for mixed audiences — from ML engineers to executives Comfort working in a fast‑moving, customer‑facing role where no two conversations are the same Willingness to travel 20–30%, including internationally Nice to Haves Prior experience in a solutions consultant, sales engineering, or customer‑facing technical role Exposure to large language models for biological sequences, diffusion models for structure, or related techniques Experience with scientific software ecosystems (LIMS, ELN, computational toolchains used in biopharma R&D) A track record of building technical assets — demos, benchmarks, write‑ups — that help others understand complex systems Our commitment to inclusive hiring Cradle evaluates all candidates based on merit, regardless of sex, gender, ethnicity, socio‑economic background, or any other aspect of identity. We maintain zero tolerance for discrimination and actively encourage candidates from all backgrounds to apply. #J-18808-Ljbffr