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3D Machine Learning Jobs in Conroe, TX (NOW HIRING)

Onesubsea

FWI & AI Scientist

Houston, TX · On-site

$125 - $150/hr

Scale implementations for GPU clusters, HPC systems, and large-scale 3D datasets. * Integrate AI ... Expertise in physics‑informed machine learning, learned solvers, or neural surrogate models.

TNT-Crane-

CAD

Houston, TX · On-site

$60 - $80/hr

If you have a solid grasp of drafting fundamentals and a passion for learning how massive, real ... Uses 2D/3D AutoCad to develop lift plans, machinery installations and supporting designs.Sets up ...

SLB

FWI & AI Scientist

Houston, TX

Scale implementations for GPU clusters, HPC systems, and large-scale 3D datasets. Integrate AI into ... machine learning, learned solvers, or neural surrogate models. Proven success applying deep ...

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Showing results 1-20

All Jobs3D Machine Learning Jobs3D Machine Learning Jobs in Conroe, TX

3D Machine Learning information

See Conroe, TX salary details

$21.8K

$36.5K

$75.3K

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

As of Sep 9, 2026, the average yearly pay for 3d machine learning in Conroe, TX is $36,457.00, according to ZipRecruiter salary data. Most workers in this role earn between $27,800.00 and $39,400.00 per year, depending on experience, location, and employer.

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 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 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 Conroe, TX?

For 3D Machine Learning jobs in Conroe, TX, the most frequently searched job titles are:

What job categories do people searching 3D Machine Learning jobs in Conroe, TX look for?

The top searched job categories for 3D Machine Learning jobs in Conroe, TX are:

What cities near Conroe, TX are hiring for 3D Machine Learning jobs?

Cities near Conroe, TX with the most 3D Machine Learning job openings:

3D Machine Learning jobs near you
Infographic showing various 3D Machine Learning job openings in Conroe, TX as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $36,457 per year, or $17.5 per hour.

Algorithm Engineer, Deep Learning & Vision (New Grad)

Houston, TX • On-site

Bot Auto
See more jobs at Bot Autoexternal

Full-time

Re-posted 13 days ago

Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
Key Responsibilities
  • Model Implementation & Iteration: Participate in the development, training, and optimization of state-of-the-art deep learning models for autonomous driving, with a focus on end-to-end architectures, including perception, online mapping, and end-to-end planning.
  • Full Lifecycle Execution: Engage in the entire machine learning workflow under the guidance of domain experts, spanning from data curation and data analysis to model experimentation, hyperparameter tuning, and rigorous performance metric verification.
  • Cross-Functional Collaboration: Partner with simulation, infrastructure, and downstream planning/control teams to deploy, evaluate, and integrate machine learning components into our production pipeline for autonomous trucks.
  • Literature Tracking: Stay abreast of the latest research breakthroughs in computer vision and generative AI, and actively bench-test promising SOTA methods to solve real-world corner cases.
How You'll Grow
This matters as much to us as what you'll ship.
  • You get a real mentor. Every engineer is paired with senior-level engineers developing you. Mentorship here is weighted toward design and judgment: how to frame a problem, what to build and why, how to tell whether a solution is actually right.
  • We promote fast. Managers are expected to push engineers to attempt work above their current level, and to promote in the next cycle when they deliver it.
Qualifications
Required:
  • Education: A Bachelor's, Master's, or Ph.D. (including upcoming graduates) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
  • You have trained neural networks. Coursework, research, personal projects, open-source work, and internships all count. We care that you have actually run the loop: built a model, trained it, found out why it was not working, and fixed it.
  • Core Knowledge: Strong theoretical foundation in machine learning and deep learning, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).
  • Technical Stack: Proficiency in Python and deep learning frameworks such as PyTorch, along with strong software engineering fundamentals (data structures, algorithms, and clean coding practices).
  • Attributes: High self-motivation, strong analytical and problem-solving skills, a fast learner in a high-velocity startup environment, and a strong team-player mindset.
Preferred:
  • Computer vision. Research or projects in computer vision, and particularly in 3D.
  • Specific Research Directions: Academic thesis or deeply focused research experience in one or more of the following domains:
    • Computer Vision (2D or 3D)
    • Online Mapping, Vectorization, or Visual SLAM
    • Prediction and Behavioral Modeling
  • Academic Achievements: A track record of research publications in machine learning, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS).
  • Engineering Plus: Hands-on experience with model deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++).
  • Industry Exposure: Prior internship experience within the autonomous driving industry or advanced robotics labs.

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