Senior ML Engineer - 3D Geometry
HILOS
Software Engineering, Data Science
Posted on Apr 5, 2025
Senior ML Engineer - 3D Geometry
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Role
HILOS is building an AI-driven design platform to enable intuitive creation and modification of manufacturable 3D geometry. We are seeking a machine learning engineer with deep expertise in 3D geometry processing and learning-based methods to join our team. Your work will directly impact the way we generate, edit, and reverse-engineer CAD-compatible shapes for digital manufacturing workflows.
The ideal candidate will bring experience in learning with 3D geometric data (point clouds, meshes, SDFs) and a strong understanding of how these models integrate with or inform CAD-based design systems. Familiarity with approaches that bridge neural models and CAD representations—such as LLMs for CAD code or geometry-to-code systems (e.g., CAD-Recode)—is a plus, though not required.
Responsibilities
Research, implement, and optimize deep learning methods for 3D geometry understanding, generation, and reconstruction using both explicit and implicit representations.
Develop pipelines that translate sparse or high-level input (e.g., sketches, point clouds, prompts) into structured and editable CAD representations.
Explore reverse engineering pipelines that extract parametric or code-based design primitives from point clouds or mesh scans.
Build scalable systems for training, evaluating, and deploying 3D ML models, with attention to manufacturability, surface fidelity, and performance.
Read and replicate state-of-the-art research papers, evaluating their applicability to internal problems and reimplementing them as needed.
Work closely with computational design and software teams to integrate ML outputs into CAD-driven workflows.
Contribute to the architecture of an AI-first 3D design platform that prioritizes ease of use and real-time interactivity.
Preferred Experience
Strong background in 3D deep learning, including experience with:
PointNet++, KPConv, sparse convolutional networks, 3D diffusion, or transformer-based models.
Implicit representation models (e.g., DeepSDF, OccNet, VolRecon).
Mesh or parametric surface reconstruction from learned representations.
Familiarity with geometry-aware ML libraries and tools (e.g., PyTorch3D, Kaolin, Trimesh, Open3D).
Ability to interpret and implement cutting-edge research from academic papers, with strong debugging and evaluation skills.
Understanding of CAD principles and parametric modeling. Experience with integrating ML models into CAD systems (e.g., Rhino, Fusion 360, Onshape) is a strong plus.
Interest or experience in CAD-code generation from ML models (e.g., LLMs that produce OpenSCAD or Grasshopper scripts).
Proficiency in Python and deep learning frameworks such as PyTorch.
Experience building end-to-end ML systems, including training pipelines, evaluation metrics, and cloud-based deployment.
Qualifications
MS or PhD in Computer Science, Machine Learning, Computer Graphics, Computational Design, or related fields.
3+ years of research or industry experience building and deploying ML models for geometric or CAD-related tasks.
Strong portfolio of publications, open-source contributions, or production-level tools involving 3D deep learning or geometric computing.
