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Mach9

Head of ML

San Francisco, California, United States

Location
San Francisco, California, United States
Work
Not stated
Employment
Full-time
Level
Director
Function
Data & AI
Pay
$275K – $400K a year
Posted
5 months ago

Pay and the market here

The top of this role’s range, $400K a year, is 27% above the median of $315K for director data jobs in San Francisco (middle half $268K – $376K, 53 jobs that state pay).

1,323 data jobs open in San Francisco: 11% remote, 283 posted in the last 30 days.

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About the role

The Role

At Mach9, we're building AI to turn dense LiDAR and image data into design-grade CAD deliverables for civil engineering and surveying. We have one of the richest survey-grade datasets in the world. Our product, Digital Surveyor, automates work that takes surveyors and engineers weeks of manual effort.

We're looking for a Head of Machine Learning to build and grow the organization that turns our data into the automated products of the future in surveying and design. This is a hands-on leadership role: you'll set the technical vision for our strategy, mentor and grow a team of ML engineers and researchers, and own the throughline from hard research problems to shipped product capabilities.

This role is ideal for experienced engineers, team leads, and managers who want to push the frontier of 3D computer vision and perception. You’ll have the opportunity to solve problems that few other teams have the data or the talent to take on. We need you to bring a strong vision of what’s possible with our unique dataset, and then to build a team around you to accomplish that vision.

Responsibilities

  • Define and drive a coherent vision for how Mach9 can leverage our data advantage to build the best automation products across surveying and design.
  • Translate your vision into a technical roadmap, execute strongly, and bring our products to the next level. You’ll need to identify which challenges are worth solving, and then which models are ready to ship.
  • Build and grow our ML team: hiring, onboarding, structuring the team as it scales, and shaping the split between research and engineering as our organization matures.
  • Mentor ML engineers and researchers, providing technical direction, career growth, and guidance to raise the level of the whole team.
  • Stay technically hands-on: review designs and code and weigh in on architecture and modeling decisions, so that you stay credible and connected to the team and problems.
  • Partner with product and engineering leadership to align Mach9’s investment into research with our product strategy and customer needs.

Requirements

  • Experience leading and growing teams — hiring and mentoring ML engineers and researchers.
  • 5+ years of work experience in machine learning, with a track record of shipping ML models or systems to production.
  • Strong technical depth in machine learning and computer vision, and an ability to set technical direction and to earn the trust of a strong technical team.
  • Ability to develop technical roadmaps and execute on them.
  • Strong communication skills, with the ability to operate across research and engineering.
  • Proficient with Python and PyTorch. You must be comfortable engaging deeply with our technical work.

Bonus qualifications

  • Background in machine learning for 3D perception — point cloud understanding, 3D detection/segmentation, geometric deep learning, or related areas.
  • Experience with CAD AI, design automation, or applying ML to generate structured/geometric output, rather than just classification or detection.
  • Experience scaling an ML organization through a growth phase — defining team structure and hiring strategy.
  • Background in domains that work with similar data to Mach9 — remote sensing, geomatics, autonomous driving, or robotics.
  • Experience leveraging large unstructured datasets, especially reality-capture datasets, to build data-driven products and flywheels.

Source

Posted by Mach9 on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published, read 07 Nov 2026 11:45 UTC; the original is the reference.

View the original ad ↗

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