Skip to main content

Mach9

ML Engineer

San Francisco$180k–$300kfulltimemidAdded 1 month ago

About this role

As an ML Engineer at Mach9, you will develop perception models for advanced AI-driven CAD systems, focusing on extracting 3D features from LiDAR data. This research-oriented role emphasizes transitioning innovative ideas into practical applications, enabling field professionals to leverage cutting-edge technology.

What you'll do

  • Design and train computer vision and 3D ML models for CAD features.
  • Translate ML research into product capabilities through prototyping and experiments.
  • Manage models throughout their lifecycle from conception to integration.
  • Create evaluation methodologies to meet surveying and engineering accuracy.
  • Collaborate with infrastructure and product teams for model deployment.

What they're looking for

  • Master's or PhD in related field or equivalent experience.
  • Strong foundation in computer vision and deep learning.
  • Experience in moving ML models from research to production.
  • Knowledge of 3D perception geometric concepts.
  • Excellent communication and collaboration skills.
  • Proficient in Python and production-quality ML libraries.
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

Mach9

Mach9 builds AI-powered CAD and surveying software for civil engineers, leveraging LiDAR data and geospatial datasets to enhance field work and design processes. The company is hiring software engineers, ML engineers, infrastructure specialists, and customer success professionals to develop its core product suite and support customer implementations.

View all jobs at Mach9

Likely interview questions

  • Walk us through a computer vision or 3D ML model you took from research prototype to production. What were the biggest challenges in that transition, and how did you address them?
  • Describe your experience working with LiDAR point cloud data or 3D detection/segmentation models. What architectures have you used, and how do you approach evaluation for 3D perception tasks?