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Basecamp Research Tech Stack
AI-driven protein design using proprietary nature knowledge graph
Basecamp Research builds machine learning systems to design proteins from first principles, trained on a proprietary knowledge graph of natural biology. The stack (Python, Go, Kubernetes, Dagster, Temporal, NVIDIA) reflects deep computational demand: petascale pipelines, containerized workflows, and orchestration at scale. Active projects span gene editing tools, protein design build-test cycles, and infrastructure scaling — suggesting the team is hitting workflow bottlenecks and cloud compute constraints as they move from prototype toward production therapeutics.
Tech Stack 10 technologies
What Basecamp Research Is Building
◆Challenges
- Bottlenecks in workflows
- Cloud compute procurement
- Targeted integration of large cargo
- Regulatory approval of gene editing products
- Scaling infrastructure for rapid growth
- Security and reliability improvements
- Streamlining operations
- Scaling r&d culture
- Scaling experimental programs
▲Active Projects
- Novel gene editing tools development
- Novel protein design build test cycles
- Self-service infrastructure
- New tooling to streamline operations
- Infrastructure scaling for next generation therapeutics
- Large-scale scientific workflows
- Containerised pipelines
- Internal tooling
- Petascale deep learning and bioinformatics pipelines
- Computational workflows for gene editing therapeutics
Hiring Activity
8 roles · 2 in 30d
Department
Seniority
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