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Ouster Tech Stack

Digital lidar and perception software for industrial robotics and autonomous systems

Automation Machinery Manufacturing San Francisco, California 201–500 employees Founded 2015 Public Company
ouster.com LinkedIn

Ouster manufactures digital lidar sensors and perception software for autonomous systems and robotics. The tech stack reveals a hardware-to-software company: C++, Python, and Rust for embedded systems; PyTorch, Kafka, and Nvidia Jetson for on-device ML; Docker, GitLab CI/CD, and Jenkins for production infrastructure. Active hiring is concentrated in engineering (14 roles) with heavy investment in senior and staff-level talent — matching their project focus on firmware, edge optimization, and real-time inference. The pain-point pattern (yield improvements, test-time reduction, manual AP workflows) signals scaling challenges in high-volume manufacturing.

Tech Stack 71 technologies

Core StackPython Jira Confluence GitLab Bitbucket Jenkins C++ Docker GitLab CI/CD Angular JavaScript Kafka AWS AWS RDS Rust NetSuite Go PostgreSQL gRPC PyTorch Subversion Bamboo LiDAR Jama Connect ISO 26262 Nvidia Jetson Raspberry Pi GCP TCP MQTT+41 more

What Ouster Is Building

◆Challenges

  • Test time reduction
  • Yield improvements
  • Gaps in current organization
  • High-volume vendor payment processing
  • Manual effort in ap
  • Optimizing models for real-time inference
  • Training with limited data
  • Adapting research to production
  • Real-time inference on-device
  • Data-constrained environments

▲Active Projects

  • Lidar hardware development
  • Unified object detection and tracking model development
  • Edge optimization for real-time inference
  • Data strategy for low-data regimes
  • Gemini portal
  • High-volume production validation
  • Electrical system architecture
  • Scalability and manufacturability projects
  • Gemini cloudviewer
  • Lidar firmware system

Hiring Activity

Accelerating25 roles · 10 in 30d

Department

Engineering
14
Finance
3
Manufacturing
2
Marketing
2
Ops
2
Sales
2
Support
1

Seniority

Senior
11
Manager
5
Mid
5
Staff
4
Lead
1
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About Ouster

Ouster is a publicly traded company (Nasdaq: OUST) that designs and manufactures digital lidar sensors and AI perception software for industrial, robotics, automotive, and smart-infrastructure applications. Founded in 2015 and headquartered in San Francisco, the company operates across the Americas, Europe, and Asia-Pacific, serving thousands of customers. Their platform combines lidar hardware with sensor fusion, on-device AI compute, and perception models. Manufacturing and validation are core operational functions: active projects span lidar hardware development, firmware systems, production validation at scale, and electrical architecture. The 201–500-person org is weighted toward engineering with concurrent investment in manufacturing and operations.

HeadquartersSan Francisco, California
Company Size201–500 employees
Founded2015
Hiring MarketsThailand, United States, France, Canada, Singapore

Frequently Asked Questions

What is Ouster's tech stack?

Core languages: C++, Python, Rust. ML/inference: PyTorch, Nvidia Jetson. Hardware: LiDAR, Raspberry Pi. DevOps: Docker, GitLab, GitLab CI/CD, Jenkins, Bamboo. Data: Kafka, PostgreSQL, AWS RDS. Also uses Jira, Confluence, NetSuite, GCP, AWS, gRPC, MQTT, and ISO 26262 certification tooling (Jama Connect).

What is Ouster working on?

Active projects include lidar hardware and firmware development, unified object detection and tracking models, edge optimization for real-time inference on-device, high-volume production validation, electrical system architecture, and the Gemini portal and cloudviewer platforms.

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How this profile is built

Ouster'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.