ZipRecruiter

Log In

1

3D Machine Learning Jobs in Kenosha, WI (NOW HIRING)

Asyst Technologies L P

Project Engineer

Kenosha, WI · On-site

Growth - We promote a culture of growth & learning We are seeking a strategic and hands-on Project ... Coordinate prototype development using 3D printing, rapid tooling, machining, and related methods

Nestle

Engineering CAD Designer

Burlington, WI

$28.50 - $39.25/hr

Our supportive and collaborative environment encourages bold ambitions and continuous learning so ... The CAD Drafter will need to be proficient in AutoCAD 2D, 3D, and Autodesk Inventor, enabling the ...

All Jobs3D Machine Learning Jobs3D Machine Learning Jobs in Kenosha, WI

3D Machine Learning information

See Kenosha, WI salary details

$24.9K

$41.5K

$85.8K

How much do 3d machine learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for 3d machine learning in Kenosha, WI is $41,541.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,700.00 and $44,900.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in 3d machine learning, and how can they be addressed?

Professionals in 3D machine learning often encounter challenges such as handling large and complex datasets, managing high computational requirements, and ensuring model robustness across diverse 3D data types (e.g., point clouds, meshes, voxel grids). Addressing these challenges typically involves using efficient data preprocessing pipelines, leveraging cloud computing or advanced GPU resources, and staying updated with the latest research on 3D data augmentation and model architectures. Collaboration with multidisciplinary teams—including data engineers, computer vision experts, and domain specialists—is also crucial for overcoming technical obstacles and producing practical, scalable solutions.

What is 3d machine learning?

3D machine learning is a field of artificial intelligence focused on developing algorithms and models that can process and understand three-dimensional data. This includes tasks such as object recognition, scene reconstruction, segmentation, and analysis using 3D data formats like point clouds, meshes, or volumetric grids. Applications of 3D machine learning are found in areas like autonomous driving, robotics, medical imaging, and augmented reality. The field combines techniques from computer vision, deep learning, and geometry processing to interpret complex spatial information.

What are the key skills and qualifications needed to thrive as a 3d machine learning engineer, and why are they important?

To thrive as a 3D Machine Learning Engineer, you need a solid background in computer science, mathematics, and experience with 3D data processing and machine learning algorithms, typically supported by a relevant degree. Expertise in tools and frameworks like Python, PyTorch or TensorFlow, and libraries such as Open3D or PCL is commonly required, along with familiarity with 3D data formats. Strong problem-solving skills, creativity, and effective communication set top performers apart in this role. These skills enable the development of innovative solutions for complex 3D data challenges, which are crucial for advancements in fields like robotics, computer vision, and AR/VR.

What is the difference between 3D Machine Learning vs 3D Computer Vision?

Aspect3D Machine Learning3D Computer Vision
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegree in Computer Vision, Computer Science, or related fields; experience with image processing
Work EnvironmentResearch labs, AI development teams, tech companiesImaging labs, robotics, autonomous vehicles, tech firms
Industry UsageDeveloping models for 3D data analysis, sensor data integrationProcessing 3D images, object detection, scene reconstruction

While 3D Machine Learning focuses on creating algorithms that learn from 3D data, 3D Computer Vision emphasizes interpreting and analyzing 3D visual information. Both fields often overlap but serve different primary objectives within AI and imaging applications.

What are popular job titles related to 3D Machine Learning jobs in Kenosha, WI? For 3D Machine Learning jobs in Kenosha, WI, the most frequently searched job titles are:
What job categories do people searching 3D Machine Learning jobs in Kenosha, WI look for? The top searched job categories for 3D Machine Learning jobs in Kenosha, WI are:
What cities near Kenosha, WI are hiring for 3D Machine Learning jobs? Cities near Kenosha, WI with the most 3D Machine Learning job openings:
3D Machine Learning jobs near you
Infographic showing various 3D Machine Learning job openings in Kenosha, WI as of June 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,541 per year, or $20 per hour.

Package & SI/PI - Senior/Staff Packaging Engineer - Electro-Thermal

Eliyan

Mundelein, IL • On-site

Full-time

Re-posted 20 days ago

Job description

Join the leading chiplet interconnect startup! We are seeking an experienced Senior/Staff Packaging Engineer specializing in electro-thermal simulation to join our advanced packaging team. You will develop comprehensive thermal and electrical simulations for next-generation semiconductor packaging solutions, including 2.5D/3D IC integration, chiplet-based systems, and advanced heterogeneous integration technologies. This role is critical in enabling high-performance computing, AI accelerators, and advanced chiplet architectures. We offer a fun work environment with excellent benefits. ONSITE M-F
Key Responsibilities:
  •  Develop detailed thermal models for 2.5D/3D IC packages, chiplets, and multi-die systems; perform steady-state and transient thermal analysis with hotspot identification
  • Execute power integrity (PI) and IR drop analysis; optimize power distribution networks (PDN) and power delivery architectures
  • Conduct electromigration (EM) and reliability analysis for interconnects, bumps, TSVs, and redistribution layers (RDL)
  • Develop chip-package co-simulation workflows using industry-standard EDA tools (ANSYS RedHawk\u0002SC, RHSC-ET, SIwave, Cadence Sigrity/Clarity)
  • Create hierarchical compact macro models (CMM) and reduced-order thermal models for early\u0002stage design optimization
  • Automate simulation workflows using Python, TCL, and Shell scripting; build design space exploration tools
  • Collaborate with silicon design, package design, and manufacturing teams on design-for-reliability (DFR) initiatives
  • Support customer engagements with technical analysis and present findings to stakeholders
Minimum Qualifications:
  •  
Education:
  • PhD in Electrical/Mechanical Engineering, or related field with focus on thermal management, power delivery, or electronic packaging (Master's with 5+ years experience considered)
  • Strong academic background in power integrity, signal integrity, and thermal management for advanced packaging
 
Technical Skills: 
  • Expert proficiency in: ANSYS RedHawk-SC, RHSC Electrothermal, Totem, PathFinder, SIwave, HFSS, Q3D; Cadence Voltus, Sigrity, Clarity; Synopsys RedHawk Fusion, PrimeTime, ICC2
  • Experience with physical design tools (Cadence Innovus, Synopsys ICC2, Siemens Calibre) and RTL\u0002to-GDSII flows
  • Strong programming/scripting: Python, C/C++, Tcl, Shell (bash); Verilog/VHDL/SystemVerilog for verification
  • Knowledge of advanced packaging: 2.5D/3D ICs (CoWoS, InFO, EMIB), chiplets, TSVs, interposers, FOWLP, RDL design
 
Domain Expertise:  
  • Deep understanding of EM/IR analysis, power integrity, thermal physics, and electrothermal co\u0002simulation
  • Expertise in heat transfer principles (conduction, convection, radiation), thermal material properties, and CTE mismatch
  • Knowledge of chiplet standards (UCIe and BoW), die-to-die interfaces, and wafer-scale integration
  • Hands-on experience with semiconductor package thermal/electrical analysis and tape-outs 
Ideal Qualifications:
  • Familiarity with machine learning applications in EDA and design optimization
  • Experience with HPC, AI/ML accelerator packaging, or co-packaged optics (CPO)
  • Background in reliability testing (thermal cycling, HTOL, THB) and measurement correlation
What we are looking for:
  • Strong analytical mindset with expertise across multiple physics domains (thermal, electrical, mechanical)
  • Excellent communication skills to present complex technical concepts to diverse audiences
  • Cross-functional collaboration abilities to work with silicon, package, product, and manufacturing teams
  • Self-motivated professional who thrives in fast-paced environments with minimal supervision
  • Continuous learner staying current with emerging technologies; innovation-driven with creative problem-solving
  • Results-oriented engineer delivering high-quality work to enable product milestones on schedule

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.