ML Engineer, Product
At a glance
Mid-level Machine Learning Engineering role at Mach9. San Francisco · full-time · $180,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Take extraction features from customer and product scoping through data planning, model training, testing and integration into Digital Surveyor
- Choose or adapt vision and point-cloud models, starting with existing model families and open-source tools where suitable
- Extend deployed models to cover new objects, geographic areas and sensor types requested by customers
- Build tests that track both gains and regressions, then refine extraction workflows using customer feedback and metrics
What you bring
- A computer science, electrical engineering or robotics degree, or equivalent experience
- Practical experience training or fine-tuning a vision model for segmentation, detection or 3D tasks
- Working knowledge of 3D perception geometry, including coordinate frames, transformations and mapping between images and point clouds
- Strong coding skills, preferably in Python and PyTorch, and comfort navigating a large codebase
Who this fits
This product-focused role suits an early-career engineer who learns quickly, explores ideas and iterates based on feedback. You’ll work directly with customers and communicate with engineers, product teams and surveyors.
From the employer
The role
At Mach9, ML Engineers build the perception models at the core of our AI-enabled CAD system. We build models to extract 3D object and line features from dense LiDAR point clouds and imagery. Our unique data advantage allows us to develop and train cutting edge 3D scene understanding models that serve real surveyors and engineers in the field.
This role is product-focused. You will be working closely with customers to develop CV/ML pipelines and workflows that solves their problems. Your responsibility is to ship new features and product lines end-to-end from data strategy, to model training, to evaluation.
This role is ideal for early-career engineers (new grad to ~3 years) who learn fast, are curious about how things work, and get real satisfaction from shipping. You need to be able to come up with ideas, try them, get feedback and iterate quickly.
Responsibilities
Ship new extraction features end to end: scoping with product and customer-facing teams, data and labeling strategy, model selection or fine-tuning, evaluation, and integration into Digital Surveyor.
Build on existing model families and open-source tooling first (vision foundation models, VLMs, point-cloud backbones, classical geometry), and know what's out there and what each is good for.
Adapt our production models to new object classes, regions, and sensor types as customers bring them.
Build evaluation pipelines that measure improvements and regression alike.
Iterate on feature extraction pipelines according to customer feedback and metrics changes.
Requirements
BS or MS in Computer Science, EE, Robotics, or a related field, or equivalent experience.
Strong coding abilities (preferably Python + Pytorch), and comfortable working with a large codebase.
Hands-on experience training or fine-tuning a vision model (segmentation, detection, or 3D) through work, internships, or projects.
Working knowledge of geometry for 3D perception: coordinate systems, transforms, projecting between images and point clouds.
Curiosity and speed: you pick up new frameworks and papers quickly and can explain what you learned to teammates.
Clear communicator with engineers, product, and the surveyors who use what you build.
Fluent with AI coding assistants.
Bonus qualifications
Have used open-source vision foundation models (SAM family, DINO, Grounding DINO, or similar) in a task before.
Experience with point clouds or LiDAR data (Open3D, PDAL, PyTorch3D, sparse convolution libraries).
Experience deploying models to production: ONNX or TensorRT, batch inference on cloud GPUs.