Perception Intern, Fall 2026

Mach9Mach9San Francisco, California, United States
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Posted

10/8/2026

Employment

Internship

Range

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Work style

On-site

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AI summary

Core responsibilities

You will contribute to the development of perception models for 2D images and 3D point clouds to detect urban infrastructure features. Additionally, you will assist in tuning machine learning models and optimizing algorithms for real-time performance on edge hardware.

Requirements overview

Candidates must be currently pursuing a degree in Machine Learning, Computer Science, or a related field with strong Python programming fundamentals. You should have hands-on experience with machine learning frameworks and a solid understanding of computer vision tasks.

Key skills

PythonMachine LearningComputer VisionPyTorchJaxAlgorithmic Problem-solvingGeospatial Data Analysis3D Point Cloud Models2D Image ModelsObject DetectionPanoptic DetectionEdge Mapping HardwareGeometry-based AlgorithmsClean Code Design

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Education requirements

bachelor degree

About Mach9

Industry

Technology, Information and Internet

Employees

40

Type

Privately Held

Size

11-50 employees

Mach9 builds software that transforms reality capture datasets into high-precision 3D maps, accelerating how teams model and understand the world. Its flagship product, Digital Surveyor, automatically extracts features like utility poles, signs, curbs, striping and more from LiDAR and imagery datasets to create engineering-ready CAD and GIS deliverables. Surveying, engineering, and GIS teams work with Mach9 to reduce the time and cost of 3D mapping and deliver projects faster than ever before. Founded in 2021 and based in San Francisco, Mach9 is backed by Quiet Capital, Y Combinator, Soma Capital, Tiger Global, and Overmatch Ventures and trusted by leading engineering, construction and infrastructure organizations.

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Job categories

TechnologyEngineeringSoftwareData & AnalyticsScience & Research

Description

About the role As a Perception Intern at Mach9, you will contribute to a range of projects that are critical to the development and enhancement of our core products. You will have the opportunity to work on advanced 2D image and 3D point cloud models for object and panoptic detection, focusing on urban and roadway features such as traffic lights, utility poles, curbs, and painted road markings. Example past and future projects: Develop an initial prototype to detect paint lines and curbs on roadways. (This project ended up developing into one of our new products). Bring our perception models and algorithms into a real-time detection domain, trading off accuracy for speed to run on an advanced edge mapping hardware system. Assist in the development and tuning of machine learning models to enhance performance and accuracy. What We're Looking For: Strong programming fundamentals in Python, with a focus on algorithmic problem-solving and clean code design. Ability to implement and iterate on heuristic or geometry-based algorithms quickly. Hands-on experience with machine learning frameworks (e.g., PyTorch, Jax) and familiarity with computer vision tasks. A keen interest in applying machine learning to solve real-world problems, particularly in the context of geospatial data analysis. Excellent problem-solving abilities and a collaborative mindset, with the capacity to work independently on complex tasks. Currently pursuing a degree in Machine Learning, Computer Science, or a related field, with a solid understanding of machine learning concepts and techniques. Why Mach9? This internship presents a unique opportunity to contribute to impactful projects at the intersection of machine learning and geospatial technology. You will work alongside industry experts, gain valuable practical experience, and help shape the future of urban infrastructure management.

Requirements

  • •Python
  • •Machine Learning
  • •Computer Vision
  • •PyTorch
  • •Jax
  • •Algorithmic Problem-solving
  • •Geospatial Data Analysis
  • •3D Point Cloud Models
  • •2D Image Models
  • •Object Detection
  • •Panoptic Detection
  • •Edge Mapping Hardware
  • •Geometry-based Algorithms
  • •Clean Code Design

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