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Calico Life Sciences Tech Stack

AI-driven drug discovery platform focused on aging biology and age-related diseases

Biotechnology Research South San Francisco, CA 201–500 employees Founded 2013 Privately Held

Calico is an Alphabet-founded biotech R&D company building computational infrastructure for drug discovery in aging. The tech stack—PyTorch, JAX, TensorFlow, AlphaFold, alongside wet-lab tools like LC-MS, qPCR, and HPLC—reflects a dual-track approach: deep learning for target identification and sequence analysis paired with classical chemistry and molecular biology. The hiring mix is research-dominated (21 roles) with a small engineering contingent (4), typical of discovery-stage biotech, though the active adoption of AlphaFold and ML frameworks suggests computational leverage is becoming central to their screening and target validation pipeline.

Tech Stack 35 technologies

Core StackPyTorch Python TensorFlow MATLAB Docker Terraform NetSuite BigQuery React Apache Spark FastAPI Kubernetes AWS Jax JAX HPLC Agilent Coupa GCP qPCR LC-MS PyMOL Benchling AlphaFold Ray TIFF Zarr transformers Flash Azure

What Calico Life Sciences Is Building

Challenges

  • Accelerating drug discovery feedback loop
  • Translational drug development pipeline
  • Random screening
  • High-quality training data
  • Computational predictions
  • Scalable synthetic processes
  • Process risk mitigation
  • Accelerating clinical development
  • Managing global registrations
  • Inefficient medical writing processes

Active Projects

  • Novel drug target identification for age-related cardiovascular diseases
  • Design and train deep learning models for biological sequence analysis
  • Advancing drug discovery programs
  • Aging biology research
  • Aging therapeutics development
  • Translational biomarkers for novel targets in neurological/neuropsychiatric indications
  • Experimental medicine studies to demonstrate proof-of-mechanism of novel targets in neuropsychiatric diseases
  • Early clinical development plans to demonstrate proof-of-concept in neuropsychiatric indications
  • Design-build-test-learn cycle
  • Antibody discovery engine

Hiring Activity

Steady30 roles · 9 in 30d

Department

Research
21
Engineering
4
Business
1
Healthcare
1
Marketing
1
Regulatory
1

Seniority

Senior
12
Director
7
Junior
3
Principal
3
Lead
2
Mid
1
Staff
1

Notable leadership hires: Medical Director

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About Calico Life Sciences

Calico develops technologies and drug candidates aimed at understanding and intervening in human aging. Founded in 2013 as an Alphabet subsidiary, the company operates a research campus in South San Francisco with 201–500 employees. The portfolio spans aging biology research, novel drug target identification (particularly in cardiovascular and neuropsychiatric domains), and a translational pipeline from biomarker discovery through clinical proof-of-concept studies. Projects include an antibody discovery engine, deep learning models for biological sequence analysis, and design-build-test-learn cycles for experimental medicine. Pain points center on accelerating feedback loops in drug discovery, scaling synthetic chemistry, and managing complex clinical and regulatory timelines.

HeadquartersSouth San Francisco, CA
Company Size201–500 employees
Founded2013
Hiring MarketsUnited States

Frequently Asked Questions

What is Calico Life Sciences working on?

Calico focuses on aging biology research, novel drug target identification for cardiovascular and neuropsychiatric diseases, deep learning for biological sequences, antibody discovery, and clinical development programs to demonstrate proof-of-concept in age-related indications.

What tech stack does Calico use?

Calico uses PyTorch, JAX, TensorFlow, AlphaFold, and Ray for ML; Python and FastAPI for software; GCP, AWS, and Azure for cloud infrastructure; and wet-lab instruments including LC-MS, qPCR, HPLC, and Agilent systems.

How this profile is built

Calico Life Sciences's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.