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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 revolutionising 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 pharma, biotech, agritech, foodtech, and academia. We're an experienced team of around 70 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. As an Engagement Manager within our Customer Success team, you’ll ensure that Cradle’s largest customers achieve success across their entire protein R&D portfolio. While your Scientific Advisor peers drive success at the project level, you will own success at the portfolio level — building deep relationships with senior stakeholders, aligning on portfolio success metrics, and ensuring our software and services deliver measurable value across departments and programs. You’ll serve as a trusted partner to senior leaders in global biopharma and industrial biotech companies, guiding strategic planning, adoption, and expansion of Cradle’s platform. Working closely with Scientific Advisors, Account Executives, and Product teams, you’ll translate Cradle’s scientific impact into business outcomes and long-term partnerships.
Job Responsibility:
Oversee post-sale success across global biotech and pharma customers, from initial onboarding to long-term, broad adoption
Define and track portfolio-level success metrics and ROI, presenting progress to senior customer stakeholders
Establish and lead steering committees to align stakeholders and ensure Cradle delivers measurable impact
Understand customer budget cycles, licensing models, and R&D roadmaps to identify timely opportunities for growth and renewals
Partner with Account Executives to expand Cradle’s footprint across new business units and therapeutic areas
Coordinate with Scientific Advisors to ensure smooth project initiation, resource allocation, and execution within licensed project slots
Manage multi-workstream engagements, ensuring alignment across Science, Product, and ML teams
Inspire and lead your cross-functional Cradle team to deliver seven star customer experiences
Act as the primary escalation and coordination point for enterprise accounts
Solicit, capture and communicate customer feedback, driving improvements in Cradle’s product and service delivery
Partner with Product and Machine Learning Research teams to align customer strategic priorities and Cradle’s roadmap
Design and co-create training and education initiatives that empower scientists to succeed on the Cradle Platform
Advocate for Cradle’s customers internally without losing sight of the delivery efficiency and interests of the Cradle team
Requirements:
Deep understanding of biopharma R&D workflows and the ability to confidently engage with executive stakeholders as well as experimental and computational scientists
5–7+ years in enterprise client-facing roles (program management, consulting, or customer success) ideally in Life Sciences
Proven ability to lead complex, multi-stakeholder programs and drive measurable ROI
Strong relationship-building, executive presence, crisp communication, and excellent organizational skills
Comfortable presenting scientific and business insights to internal and external cross-functional audiences
MSc or advanced degree in Molecular Biology, Biotechnology, Bioengineering, or a related field, or equivalent professional experience
Willingness to travel to- and work from customer sites (20% of the time)
Nice to have:
Experience managing enterprise SaaS deployments
Familiarity with services commercials (SOWs, budgeting, resourcing, and change orders)
Experience with machine learning applications in life sciences or biotech