ML Engineer, Research
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
- Build and assess vision and 3D models that pull CAD-ready shapes and features from LiDAR scans and images
- Test new research ideas, then decide which approaches can become useful product capabilities
- Take models from defining the problem and preparing data through training, evaluation and integration into Digital Surveyor
- Create accuracy measures that reflect the needs of surveying and engineering work
- Coordinate with infrastructure and product teams to scale model execution and match outputs to user needs
What you bring
- A master's or doctorate in machine learning, computer vision or computer science, or equivalent industry experience
- Hands-on deep learning work training models for segmentation, detection or 3D scene understanding
- Experience carrying an ML model from an early research build into a production feature
- Understanding of 3D geometry, including coordinate systems and transformations, plus Python and a production ML library
Who this fits
This role suits an early-to-mid-career ML engineer who wants ownership from research through product delivery. The work is based in San Francisco.
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 both research-driven and product-focused. You'll design and train the models that power our automated extraction pipeline — image and 3D detection and localization — and work end-to-end from research prototype to production feature. You'll partner closely with infrastructure and product teams to take ideas from a paper to deployed capabilities.
This role is ideal for early-to-mid-career ML engineers who thrive on end-to-end ownership and are able to move fluidly from dissecting a new architecture paper to shipping the product feature that the resulting ML model backs.
Responsibilities
Design, train, and evaluate computer vision and 3D ML models for extracting CAD-grade geometry and features from dense LiDAR and imagery.
Drive ML research that translates directly into product capabilities: prototyping new approaches, running experiments, and identifying what’s shippable.
Own models through the full product lifecycle: problem framing, data strategy, training, evaluation, and final integration into our cloud-based CAD software, Digital Surveyor.
Develop evaluation methodology and metrics that reflect real surveying and engineering accuracy requirements.
Work with ML infrastructure engineers to scale training and inference of your models and with product teams to align your model’s behavior with what the user wants.
Requirements
Master's or PhD in Machine Learning, Computer Vision, Computer Science, or a related field, or equivalent industry experience.
Strong foundation in computer vision and deep learning, with hands-on experience training models for segmentation, detection, or 3D understanding.
Experience taking a ML model from research/prototype to production, not just publishing or benchmarking.
Working knowledge of geometric concepts relevant to 3D perception like coordinate systems and 3D transforms.
Strong communication skills and the ability to collaborate with researchers, other engineers and product stakeholders.
Proficient with Python and a production-quality ML library like PyTorch, JAX, or TensorFlow.
Bonus qualifications
Experience with common 3D deep learning architectures, like point cloud backbones such as PTv3, sparse convolutions, or 3D detection/segmentation networks.
Experience with large unstructured datasets — imagery and 3D point clouds — at scale.
Experience delivering production-grade models with optimization techniques such as quantization, pruning, distillation, or runtime acceleration (e.g., TensorRT, ONNX Runtime).
Familiarity with multi-GPU training and experiment management (Weights & Biases or similar).
Publications or strong open-source contributions in computer vision or 3D machine learning.