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Geometric Modeling Jobs in California (NOW HIRING)

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

... models for processing reality capture data, contributing to automated progress tracking and scene ... geometric learning. • Strong expertise in Python and deep learning frameworks: PyTorch ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent ... Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene ...

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Geometric Modeling information

What is geometric modeling?

Geometric modeling is the mathematical and computational process of representing the shape and structure of objects in two or three dimensions. This field is widely used in computer-aided design (CAD), computer graphics, animation, engineering, and architecture. Geometric modeling involves creating and manipulating digital models using points, lines, curves, surfaces, and solids, allowing for visualization, simulation, and fabrication of real-world objects. Professionals in this area use specialized software to design complex structures, analyze their properties, and prepare them for manufacturing or virtual rendering.

What are the key skills and qualifications needed to thrive as a geometric modeling specialist?

To thrive as a Geometric Modeling Specialist, you need strong mathematical proficiency, spatial reasoning, and experience with 3D modeling, often supported by a degree in computer science, engineering, or a related field. Familiarity with CAD software, 3D modeling tools (such as Blender, Maya, or SolidWorks), and programming languages like Python or C++ is typically required. Attention to detail, creativity, and effective problem-solving skills are crucial soft skills for this role. These abilities ensure the accurate creation and manipulation of complex models essential for product design, animation, simulation, and engineering applications.

What are some typical challenges faced by geometric modeling professionals in a collaborative project environment?

Geometric modeling professionals often work as part of multidisciplinary teams, collaborating closely with engineers, designers, and software developers. A common challenge is ensuring that complex models meet both aesthetic and functional requirements, while staying compatible with different software platforms used by the team. Clear communication and iterative feedback are essential to resolve discrepancies and maintain model integrity throughout the project lifecycle. Balancing technical accuracy with project deadlines is also a frequent aspect of the role.

What is the difference between Geometric Modeling vs CAD Designer?

AspectGeometric ModelingCAD Designer
CredentialsKnowledge of geometry, CAD software, and 3D modelingSimilar, often requires CAD software proficiency and technical drawing skills
Work EnvironmentDesign studios, engineering firms, manufacturingArchitectural firms, engineering companies, manufacturing
Industry UsageUsed in product design, animation, simulationUsed in drafting, technical drawings, project planning
Search & Comparison IntentFocus on 3D modeling techniques and softwareFocus on technical drafting and design documentation

While both roles involve design and technical skills, Geometric Modeling primarily focuses on creating 3D models and shapes, often for engineering or animation purposes. CAD Designers concentrate on technical drawings and detailed plans used for manufacturing or construction. Understanding these differences helps in choosing the right career path or job search focus.

What are popular job titles related to Geometric Modeling jobs in California? For Geometric Modeling jobs in California, the most frequently searched job titles are:
What job categories do people searching Geometric Modeling jobs in California look for? The top searched job categories for Geometric Modeling jobs in California are:
What cities in California are hiring for Geometric Modeling jobs? Cities in California with the most Geometric Modeling job openings:
Geometric Modeling jobs near you

Member of Technical Staff, Protein Design

Radical Numerics, Inc

San Francisco, CA • On-site

Full-time

Posted 15 days ago

Job description

About Us
Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code of life, and our purpose is to reduce human suffering.
Our team created Evo, and started the field of generative genomics. Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.
Radical Numerics is bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We've redesigned the foundation model training stack to turn the world's raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.
The same generative breakthroughs that enable life-saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend.
About the Role
As a Member of Technical Staff, Protein Design, you will develop advanced machine-learning systems at the frontier of molecular modeling,from protein language models to structure prediction and beyond.
You will work at the intersection of large-scale biological models, geometric deep learning, and structural biology. The role spans model development, training, evaluation, and scientific analysis, with a strong emphasis on building systems that generalize beyond standard benchmarks.
This is a hands-on research and engineering role. You will be expected to implement models, run large-scale experiments, diagnose failure modes, and develop rigorous ways to evaluate scientific performance. You will collaborate closely with researchers across machine learning, computational biology, and biological modeling.
What You'll Do
  • Develop and improve machine-learning models for protein structure prediction, design, and related structural biology tasks.
  • Train and fine-tune protein language models, geometric neural networks, diffusion models, and other modern scientific machine-learning architectures.
  • Explore new architectures and learning objectives for modeling protein sequence and structure.
  • Build reliable data pipelines and evaluation systems for structural modeling.
  • Design rigorous benchmarks that measure generalization and minimize data leakage or memorization.
  • Evaluate models using established structural accuracy, confidence, and physical-validity metrics.
  • Analyze model performance across diverse proteins, structural classes, and biological contexts.
  • Run ablation studies and controlled experiments to understand the impact of model architecture, data, scale, and training methodology.
  • Improve the efficiency and reliability of model training and inference on large-scale compute systems.
  • Collaborate with scientists and engineers to translate research advances into robust modeling capabilities.

What We're Looking For
  • Strong experience developing machine-learning models for protein structure prediction, structural biology, geometric deep learning, or a closely related area.
  • Experience training or fine-tuning protein language models, structure models, diffusion models, or other large scientific machine-learning systems.
  • Deep understanding of modern protein structure-prediction and design methods, loss objectives, and architectures.
  • Familiarity with geometric neural networks, equivariant architectures, pairwise representations, and generative modeling of molecular structure.
  • Strong knowledge of protein structure, including secondary and tertiary structure, protein domains, complexes, conformational flexibility, and evolutionary constraints.
  • Experience building and validating biological datasets and controlling for data leakage, homology, and benchmark contamination.
  • Familiarity with commonly used protein structure metrics and evaluation practices.
  • Fluency in Python and a modern deep-learning framework such as PyTorch or JAX.
  • Experience with distributed training, accelerators, large datasets, and reproducible experimentation.
  • Strong experimental judgment and the ability to distinguish genuine scientific progress from benchmark artifacts.
  • Ability to independently move between research, implementation, experimentation, and scientific analysis.
  • Clear communication skills and an ability to collaborate across machine learning, computational biology, and engineering.

Expertise in machine learning, computational biology, structural biology, biophysics, computer science, or a related field, or an equivalent record of research and engineering impact.
Nice to Have
  • Contributions to protein structure-prediction systems, protein foundation models, geometric generative models, or widely used structural biology software.
  • Familiarity with multiple sequence alignments, templates, coevolutionary methods, inverse folding, molecular simulation, or energy-based modeling.
  • Experience modeling other macromolecules, small molecule systems, and molecular interactions.
  • Understanding of over major protein structure datasets, benchmarks, or community evaluation efforts.
  • Experience modeling protein complexes or alternative conformational states.
  • Experience with SE(3)- or E(3)-equivariant architectures, diffusion models, flow matching, or generative modeling of molecular coordinates.
  • Experience evaluating model confidence, uncertainty, and calibration.
  • Familiarity with experimental methods for determining protein structure.
  • A record of publications, open-source contributions, or production systems demonstrating impact in generative modeling, molecular design, or scientific machine learning.

Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver's license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.
Radical Numerics participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

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