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SandboxAQ Tech Stack

AI + quantum software for life sciences, financial services, and national security

Software Development Palo Alto, CA 51–200 employees Founded 2021 Privately Held

SandboxAQ applies AI and quantum-informed models to high-stakes domains—life sciences, financial services, navigation, cybersecurity, and national security. The tech stack is heavily oriented toward scientific computing (PyTorch, TensorFlow, JAX, NumPy, SciPy, C/C++) paired with modern cloud infrastructure (AWS, GCP, Kubernetes, Kafka), signaling a company translating research models into production systems at scale. The hiring profile (mostly staff and senior engineers, plus a strong research team) and pain-point pattern (prototype-to-production friction, productizing scientific code, ML deployment scaling) reveal the core operational challenge: converting academic-grade research into reliable commercial software.

Tech Stack 31 technologies

Core StackPython AWS PyTorch TensorFlow NumPy Pandas React Kotlin Go Rust JavaScript TypeScript LangChain Java C++ Kubernetes Kafka Docker Terraform GNSS GCP Embedded Linux AWS EMR Codex SciPy C/C++ Hugging Face Transformers JAX CI/CD DO-178C

What SandboxAQ Is Building

Challenges

  • Productizing scientific innovations
  • Managing massive data transformations
  • Improving drug discovery pipeline efficiency
  • Integrating research models into production
  • Prototype to production transition
  • Efficient enablement of cutting-edge technologies
  • Scalable production deployment of ml models
  • Rapidly building ai-first products
  • Deploying scientific models to production
  • Translating research code to production

Active Projects

  • Automation of r&d workflows
  • Framework for wrapping scientific logic
  • Client contract technical delivery
  • Affinity prediction model integration
  • Ai generation engine (saige)
  • Protein-ligand co-folding model development
  • Prototype to production transition
  • Development of computational chemistry tools
  • Model development and deployment roadmaps
  • Data pipelines for lqms

Hiring Activity

Accelerating25 roles · 20 in 30d

Department

Engineering
11
Research
7
Product
6
Data
1
Executive
1
Ops
1

Seniority

Staff
12
Senior
7
Director
3
Mid
2
Intern
1
Principal
1
VP
1

Notable leadership hires: Corporate Development Director

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About SandboxAQ

SandboxAQ emerged as an independent company from Alphabet Inc. in 2022, building Large Quantitative Models (LQMs) for regulated, high-impact industries. The company operates across life sciences (protein modeling, drug discovery), financial services, navigation, cyber resilience, and national security verticals. Active projects span automation of R&D workflows, computational chemistry tools, protein-ligand co-folding models, and data pipelines for LQM deployment. With 51–200 employees and a team skewed toward research and senior engineering roles, the organization is structured to bridge scientific discovery and production deployment.

HeadquartersPalo Alto, CA
Company Size51–200 employees
Founded2021
Hiring MarketsUnited States

Frequently Asked Questions

What is SandboxAQ's tech stack?

Python, PyTorch, TensorFlow, JAX, NumPy, SciPy, C/C++, AWS, GCP, Kubernetes, Kafka, React, TypeScript, and LangChain. The stack reflects a focus on scientific computing and cloud-native ML deployment.

What does SandboxAQ work on?

AI-powered Large Quantitative Models for life sciences (protein folding, drug discovery), financial services, navigation, and cybersecurity. Current projects include automation of R&D workflows, computational chemistry tools, and production deployment of scientific models.

How this profile is built

SandboxAQ'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.