Cradle
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About
Cradle is an AI-driven protein engineering platform designed to help R&D teams design better proteins faster. It bridges the gap between digital design and wet lab validation by allowing researchers to generate protein sequences with specific desired properties, such as improved thermal stability, binding affinity, or expression levels. By leveraging generative AI, the platform suggests candidates that are more likely to succeed in the lab, reducing the traditional trial-and-error approach that often takes years in biopharma and industrial biotechnology. The workflow follows an iterative design cycle where users start by uploading their existing experimental or screening data. Cradle’s proprietary models analyze this data to understand the relationship between protein sequences and their measured properties. Researchers can then generate thousands of new candidates using specific design strategies and constraints. The platform provides detailed reports with predictive performance scores, allowing teams to select the most promising sequences for synthesis and testing. As new assay data is uploaded from the lab, the models continuously retrain and improve, creating a compounding effect on results over multiple rounds of optimization. The platform is purpose-built for scientists and engineers in the biopharmaceutical and industrial biotech sectors. It supports a wide range of protein types, including antibodies, enzymes, vaccines, and peptides. Whether a team is focused on hit identification, lead optimization, or stabilizing antigens for vaccine development, Cradle provides a scalable infrastructure that does not require a background in machine learning or computer science to operate. This makes it accessible to bench scientists who want to integrate advanced computational tools into their existing research workflows. What sets Cradle apart is its privacy-first approach and its own internal wet lab. Unlike many AI tools that use customer data to improve general models, Cradle ensures that a company’s proprietary data is used only for their specific projects and never shared or used to train models for others. Additionally, the company operates its own high-throughput lab in Amsterdam to pre-train models on high-quality biological data, ensuring they are performant from day one. The business model is also notable for being a pure software subscription service, meaning users retain full ownership of their intellectual property without being subject to royalties on discovered products.
Pros & Cons
Accelerates development timelines by 2-12x through compounding AI learning cycles
Supports simultaneous optimization of multiple properties like activity and stability
Ensures complete IP ownership for users with a no-royalty subscription model
Guarantees data privacy by never using customer data to train models for others
Offers an intuitive interface accessible to bench scientists without ML expertise
Requires existing experimental data to reach peak model performance for specific projects
Some advanced features like custom predictors are currently limited to beta access
Specific pricing details are not publicly listed and require direct sales contact
Use Cases
Biopharma R&D teams can optimize antibody binders to improve affinity while reducing unwanted immunogenicity
Enzyme engineers can accelerate specific catalytic conversions and improve stability under challenging industrial conditions
Vaccine researchers can stabilize antigens and reach therapeutic goals faster through AI-guided design and testing
Industrial biotech firms can co-optimize protein expression and activity to lower overall production costs
Peptide developers can balance desired efficacy with improved molecular stability for better therapeutic outcomes
Platform
Task
Features
• high-throughput lab data pre-training
• custom predictor support
• soc 2 compliant data security
• 3d sequence and mutation exploration
• predictive performance reporting
• experimental data integration
• multi-property co-optimization
• generative ai candidate design
FAQs
Does Cradle own the intellectual property of proteins designed on the platform?
No, Cradle operates as a software subscription service and does not claim royalties or ownership of discovered sequences. Users retain full ownership of all their intellectual property.
Is my experimental data used to train models for other companies?
No, Cradle utilizes a privacy-first approach where your data is strictly used to train custom models for your organization. Your data is never used to train or improve models for other customers.
Do I need a background in machine learning to use the platform?
The interface is specifically designed for biologists and protein engineers to use without needing a PhD in computer science. Users can generate candidates and analyze reports through an intuitive UI.
What types of proteins can the platform optimize?
The platform is versatile and works with various formats including antibodies, enzymes, vaccines, and peptides. It can optimize any property that is measurable, such as activity, binding, and stability.
How does the platform handle data security?
Cradle is fully SOC 2 compliant and employs bank-grade security protocols. It also supports Single Sign-On (SSO) through Google and Microsoft for secure and simple account management.
Pricing Plans
Subscription
Unknown Price• AI candidate generation
• Multi-property optimization
• SOC 2 compliance
• Private data security
• Custom predictors (BETA)
• Dedicated support
Job Opportunities
Backend Software Engineer, Python
Accelerate protein discovery and optimize properties like stability and binding with AI models that learn from experimental data to reduce wet lab cycles.
Benefits:
competitive salary
generous equity stake
wide range of benefits
career progression opportunities
Experience Requirements:
Extensive industry experience in developing back-end systems in a modern cloud environment
Other Requirements:
The Python programming language and its ecosystem (FastAPI, uv, ruff, pyright, …)
Databases and big data systems
Production systems for machine learning applications
Building containerized backend systems (Docker, Kubernetes)
Highly-available distributed cloud systems
Responsibilities:
Designing and developing a service-oriented decoupled web application
Architecting ways to manipulate and store biological sequence data
Architecting APIs across which we interact with machine learning models
Providing technical leadership for software engineering process
Collaborating with biologists, machine learning experts, and scientists
Show more details
Scientific Advisor, Antibodies
Accelerate protein discovery and optimize properties like stability and binding with AI models that learn from experimental data to reduce wet lab cycles.
Benefits:
competitive salary
generous equity stake
wide range of benefits
career progression opportunities
Education Requirements:
PhD in molecular biology, bioengineering, bioinformatics, or a related field
Experience Requirements:
5+ years of experience in biopharma in a scientific role in protein engineering, focusing on antibodies
Other Requirements:
Deep knowledge of the biopharmaceutical R&D process, methods, and tooling
Excellent analytical and communication skills
Ability to build strong relationships with a variety of stakeholders
Experience with machine learning (nice-to-have)
Proficiency in Python and relevant data science libraries (nice-to-have)
Responsibilities:
Advise on identifying protein optimisation projects
Translate scientific goals of projects into objectives for the Cradle platform
Understand customer assays and workflows
Ensure quality/formatting of customer data is suitable for machine learning
Train customers to use Cradle’s platform
Show more details
Scientific Advisor, Computational Protein Design
Accelerate protein discovery and optimize properties like stability and binding with AI models that learn from experimental data to reduce wet lab cycles.
Benefits:
competitive salary
generous equity stake
wide range of benefits
career progression opportunities
Education Requirements:
PhD in bioengineering, bioinformatics, molecular biology, structural biology, or a related field
Experience Requirements:
5+ years of experience in a scientific role directly related to protein engineering in industrial biotech or biopharma
Other Requirements:
Proficiency in data analysis using Python and relevant libraries
Deep knowledge of the protein engineering R&D process
Demonstrated interest in AI-guided protein design approaches
Experience with customer-facing roles in biotech (nice-to-have)
Experience with applying machine learning to protein engineering (nice-to-have)
Responsibilities:
Advise on identifying protein optimisation projects
Translate scientific goals of projects into objectives for the Cradle platform
Understand customer assays and workflows
Ensure quality/formatting of customer data is suitable for machine learning
Train scientists to use Cradle’s platform
Show more details
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