Machine Learning Engineer
New York, NY · On-site
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices)
Machine Learning Engineer
New York, NY · On-site
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices)
Machine Learning Engineer
Manhattan, NY · On-site
$150 - $190/hr
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices). #J-18808-Ljbffr
Machine Learning Engineer
Manhattan, NY · On-site
$150 - $190/hr
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices). #J-18808-Ljbffr
Machine Learning Engineer
Manhattan, NY · On-site
$140 - $190/hr
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices). #J-18808-Ljbffr
Machine Learning Engineer
Manhattan, NY · On-site
$140 - $190/hr
Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices). #J-18808-Ljbffr
Proficiency in Python, including geospatial analysis and packages (e.g., GeoPandas, Shapely) * Demonstrated ability to work with census data, PUMS, and other economic and demographic datasets
Proficiency in Python, including geospatial analysis and packages (e.g., GeoPandas, Shapely) * Demonstrated ability to work with census data, PUMS, and other economic and demographic datasets
Proficiency in Python, including geospatial analysis and packages (e.g., GeoPandas, Shapely) * Demonstrated ability to work with census data, PUMS, and other economic and demographic datasets
Quick apply
Proficiency in Python, including geospatial analysis and packages (e.g., GeoPandas, Shapely) * Demonstrated ability to work with census data, PUMS, and other economic and demographic datasets
Shapely information
What is a Shapely job?
Shapely jobs typically refer to roles that involve working with the Shapely library, a popular Python package used for manipulation and analysis of planar geometric objects. Professionals in these positions often use Shapely for tasks in geographic information systems (GIS), spatial data analysis, and mapping. Job responsibilities may include developing geospatial applications, processing and analyzing spatial data, and integrating Shapely with other Python libraries such as GeoPandas. These roles are commonly found in industries like urban planning, environmental science, and data analytics. Having strong Python skills and experience with spatial data is usually required.
What are the key skills and qualifications needed to thrive as a Shapely web developer?
To thrive as a Shapely web developer, you need proficiency in web development languages such as HTML, CSS, JavaScript, and familiarity with WordPress, alongside experience with responsive design principles. Knowledge of the Shapely WordPress theme, page builders, plugins, and version control systems like Git is typically required. Strong problem-solving abilities, attention to detail, and effective communication help developers efficiently address client needs and collaborate with teams. These skills ensure the development of attractive, functional, and user-friendly websites that meet client specifications.
What are some common challenges faced by software engineers working on the Shapely library, and how can job seekers prepare for them?
Software engineers contributing to the Shapely library often encounter challenges related to geometric algorithms, spatial data precision, and cross-platform compatibility. Debugging complex geometry issues and ensuring robust handling of edge cases require a solid understanding of computational geometry and Python development. Collaboration with GIS professionals and open-source contributors is also a key part of the role, so strong communication skills and experience with open-source workflows are valuable. Familiarity with related libraries like GEOS and understanding spatial data formats can help job seekers excel.
What is the difference between Shapely vs GIS Analyst?
| Aspect | Shapely | GIS Analyst |
|---|---|---|
| Required Credentials | Basic programming knowledge, Python skills | Bachelor's degree in Geography, GIS, or related field |
| Work Environment | Programming, data analysis, scripting | Mapping, spatial data analysis, report creation |
| Industry Usage | Used in GIS software development and data processing | Applied in urban planning, environmental management, and more |
| Common Search Intent | Data manipulation, spatial analysis in Python | GIS data analysis, mapping projects |
Shapely is a Python library for geometric operations and spatial data manipulation, often used by developers and data analysts. GIS Analysts perform broader spatial data analysis, mapping, and reporting within GIS software. While Shapely focuses on geometric calculations, GIS Analysts handle comprehensive spatial projects. Both roles overlap in spatial data handling but differ in scope and tools used.
What are popular job titles related to Shapely jobs in Garfield, NJ?
For Shapely jobs in Garfield, NJ, the most frequently searched job titles are:
What job categories do people searching Shapely jobs in Garfield, NJ look for?
The top searched job categories for Shapely jobs in Garfield, NJ are:
Full-time
Re-posted 15 days ago
Job description
About the company
Root Access is a frontier electronics company. We are a NYC-based startup funded by top investors. Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.
Core Responsibilities
Required Technical Skills & Qualifications
Root Access is a frontier electronics company. We are a NYC-based startup funded by top investors. Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.
Core Responsibilities
- Architect Physics Foundation Models: Design and train deep learning models.
- Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP, Gerber) into continuous space data.
- Multi-Modal Architecture Integration: Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines.
- Optimize for Real-Time Execution: Optimize training and inference pipelines on GPU clusters.
Required Technical Skills & Qualifications
- Education: Master's or Ph.D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML).
- Deep Learning Frameworks: 4+ years of expert-level experience with PyTorch or JAX.
- SciML Expertise: Direct, hands-on experience building and training PINNs, FNOs, etc.
- Mathematical Depth: Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic differentiation (autograd), and numerical optimization algorithms (Adam, L-BFGS).
- Data Pipelines: Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).