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Weights & Biases Tech Stack

MLOps and LLMOps platform for AI developers and foundation model teams

Software Development San Francisco, California 201–500 employees Founded 2017 Privately Held
wandb.ai LinkedIn

Weights & Biases operates a developer-first MLOps and LLMOps platform built on PyTorch, TensorFlow, JAX, and Kubernetes, with deployment across GCP, AWS, and Azure. The hiring mix—53 engineering, 20 product, 11 support roles, weighted toward senior and staff levels—reflects a company scaling product velocity and developer adoption simultaneously. Active projects span evals systems, experiment tracking visualization, sweeps expansion, and onboarding automation, while infrastructure and service reliability remain top internal friction points.

Tech Stack 64 technologies

Core StackKubernetes PyTorch TensorFlow Python Go TypeScript GraphQL React JavaScript Snowflake MySQL PostgreSQL ClickHouse Kafka Terraform AWS Docker Helm LangChain JAX CoreWeave Bigtable Pub/Sub GCP Azure marimo Weave Keras PyTorch Lightning Streamlit+34 more

What Weights & Biases Is Building

◆Challenges

  • Improving service reliability
  • Reducing fragmentation
  • Infrastructure challenges accelerating deep learning workflows
  • External dependency reliance
  • Performance scalability challenges
  • Scaling ai/ml workflows
  • Data inefficiency in training
  • Reducing onboarding friction
  • Scaling experiments to clusters
  • Meeting support slas

▲Active Projects

  • Developer community engagement program
  • Scalable onboarding and adoption motions
  • Molab
  • Evals system development
  • Diagnostic scripts and automation for internal troubleshooting
  • Onboarding content creation
  • Reusable technical assets for self-serve success
  • Core ui components development
  • Sweeps functionality expansion
  • Experiment tracking and visualization platform

Hiring Activity

Accelerating95 roles · 35 in 30d

Department

Engineering
53
Product
20
Support
11
Data
5
Sales
5
Design
2

Seniority

Senior
55
Staff
17
Mid
11
Director
6
Manager
6
Junior
1
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About Weights & Biases

Weights & Biases provides a unified system of record for AI developers and teams building, fine-tuning, and deploying machine learning models. The platform consists of two main solution areas: W&B Models for foundation model builders managing production training and fine-tuning workflows, and W&B Weave for software developers tracking and evaluating LLM applications. The company operates at scale—trusted by over 1,000 organizations including teams at OpenAI, Meta, NVIDIA, Cohere, Toyota, Square, Salesforce, and Microsoft. Based in San Francisco with 201–500 employees, W&B is actively hiring across engineering-heavy and product functions, with operations in the United States and United Kingdom.

HeadquartersSan Francisco, California
Company Size201–500 employees
Founded2017
Hiring MarketsUnited Kingdom, United States

Frequently Asked Questions

What is Weights & Biases tech stack built on?

Core ML frameworks: PyTorch, TensorFlow, JAX. Infrastructure: Kubernetes, Docker, Helm on GCP, AWS, Azure. Data layer: Snowflake, PostgreSQL, MySQL, ClickHouse. Compute and messaging: Pub/Sub, Kafka, Bigtable. Frontend: React, TypeScript, JavaScript, GraphQL.

What is Weights & Biases currently building?

Active projects include evals system development, experiment tracking and visualization, sweeps functionality expansion, developer community engagement, scalable onboarding automation, and reusable technical assets for self-serve adoption. Internal focus areas: service reliability, infrastructure scaling for deep learning, and reducing onboarding friction.

How this profile is built

Weights & Biases'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.