Cambridge Future Tech
VP Engineering (Platform & Bioinformatics) | GitLife Biotech
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GitLife Biotech
GitLife Biotech is building a new category at the intersection of software and engineering biology. At the core of the platform is CellRepo — a Git-Style system for version-controlling biological assets, linking physical cells to digital records, and enabling traceable, trusted, and reproducible biology.
The next phase requires a senior engineering leader who can turn this platform into a commercially robust product, while embedding bioinformatics and computational tooling directly into the workflow. We are looking for a hands-on, product-minded VP Engineering to lead the development of GitLife’s platform.
This role combines:
- Strong technical execution and delivery
- Architecture ownership of a complex, data-driven system
- Partnership with domain experts in synthetic biology
The VP Engineering will be responsible for building a scalable, production-grade platform, while evolving the system toward integrated bioinformatics and AI-enabled workflows. You will be expected to:
- Build and scale the CellRepo platform into a secure, reliable, and commercially deployable product
- Work closely with the CEO and leadership team on product and company strategy, as well as synthetic biology leadership on domain requirements and workflows
- Establish a strong engineering foundation across architecture, infrastructure, and delivery
- Develop the platform into a bio-digital system that integrates biological data, experimental workflows, and computational analysis
- Ensure the system supports traceability, provenance, and reproducibility of engineered biology
- Translate scientific needs into robust technical systems
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What Your Responsibilities Will Be
Platform Engineering & Delivery
- Own architecture and technical delivery of the GitLife platform
- Build a production-grade system capable of enterprise and regulated deployments
- Define best practices across system design, testing and reliability, and security and data integrity
- Develop the roadmap for open API capabilities, AI agent / MCP-based integrations, conversational workflows inside the product, and bioinformatics and domain-specific tooling
- Lead end-to-end product delivery from concept to release
- Ensure the platform supports high data integrity and auditability
Bioinformatics & SynBio Tooling
- Build and integrate bioinformatics capabilities directly into the platform
- Develop tools to support sequence analysis and validation, tracking and classification of genetic modifications, and linking experimental data with version-controlled records
- Enable workflows that connect design → build → test → learn cycles
- Integrate with external bioinformatics pipelines and scientific data sources
Engineering Team Leadership
- Assess current technical capabilities and identify gaps
- Define the future engineering team structure
- Hire and mentor engineers across backend / platform, data / bioinformatics, and infrastructure
- Establish an execution culture focused on quality, speed, and ownership
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What Experience You Need
Essential
- Strong background in software engineering and platform architecture
- Experience delivering production-grade systems, ideally in startup or scaling environments
- Experience in bioinformatics, computational biology, or life sciences software
- Proven ability to balance hands-on development and leadership
- Experience building APIs, distributed systems, or data-heavy platforms
- Strong judgement in prioritisation and technical trade-offs
Highly Desirable
- Experience working with genomic or sequencing data and scientific workflows or lab systems
- Strong programming capability (e.g. Python or similar for data-intensive systems)
- Familiarity with bioinformatics pipelines and data visualisation or analysis tools for biological systems
- Experience abstracting complex workflows into usable, intuitive products for scientists
Who We Are Looking For
- Strong builder mindset — focused on execution and delivery
- Comfortable operating in ambiguity and early-stage environments
- Able to bridge software engineering and scientific domains
- Pragmatic, with strong judgement on where to simplify vs invest
- Collaborative and credible across both technical and scientific teams
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