1

Machine Learning Object Detection Jobs in Missouri

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Build predictive models and machine learning algorithms. * Combine models through ensemble modeling ... Computer vision-related experience with torchvision or tensorflow object detection APIs.

Build predictive models and machine learning algorithms. * Combine models through ensemble modeling ... Computer vision-related experience with torchvision or tensorflow object detection APIs.

Principal, Data Scientist

Cassville, MO · On-site

$110K - $220K/yr

The Finance Retail & Audit Analytics (FRAA) organization is investing heavily in next-generation AI, Machine Learning, and Data Science capabilities that enable anomaly detection, predictive insights ...

Principal, Data Scientist

Anderson, MO · On-site

$110K - $220K/yr

The Finance Retail & Audit Analytics (FRAA) organization is investing heavily in next-generation AI, Machine Learning, and Data Science capabilities that enable anomaly detection, predictive insights ...

Principal, Data Scientist

Noel, MO · On-site

$110K - $220K/yr

The Finance Retail & Audit Analytics (FRAA) organization is investing heavily in next-generation AI, Machine Learning, and Data Science capabilities that enable anomaly detection, predictive insights ...

(USA) Staff, Data Scientist

Cassville, MO · On-site

$110K - $220K/yr

Design, train, and deploy computer vision models for object detection, image-based personalization ... MS or (preferably) PhD in Computer Science, Machine Learning, Computer Vision, Applied Mathematics ...

(USA) Staff, Data Scientist

Anderson, MO · On-site

$110K - $220K/yr

Design, train, and deploy computer vision models for object detection, image-based personalization ... MS or (preferably) PhD in Computer Science, Machine Learning, Computer Vision, Applied Mathematics ...

(USA) Staff, Data Scientist

Noel, MO · On-site

$110K - $220K/yr

Design, train, and deploy computer vision models for object detection, image-based personalization ... MS or (preferably) PhD in Computer Science, Machine Learning, Computer Vision, Applied Mathematics ...

As a Staff Machine Learning Engineer , you will play a key role in building and implementing ... drift detection at scale. * Software Engineering Rigor: Strong background in Python, distributed ...

Develop and deploy machine learning, predictive analytics, and prescriptive analytics, including time-series anomaly detection, predictive maintenance, soft sensors, forecasting, and Digital Twins.

next page

Showing results 1-20

Machine Learning Object Detection information

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

To excel as a Machine Learning Object Detection Engineer, you need a solid background in computer science, mathematics, and deep learning principles, often backed by a relevant degree and experience in computer vision. Familiarity with frameworks like TensorFlow, PyTorch, and OpenCV, as well as experience with annotation tools and GPU computing, is typically required. Strong problem-solving abilities, attention to detail, and effective communication are vital soft skills for collaborating with cross-functional teams and addressing complex challenges. These competencies ensure accurate model development, efficient deployment, and continual improvement of object detection systems in real-world applications.

What are some common challenges faced when working on machine learning object detection projects?

One of the main challenges in machine learning object detection roles is dealing with the quality and quantity of annotated data, as accurate labeling is essential for model performance. Another common challenge is managing variations in object scale, lighting, and occlusion within real-world images, which can affect detection accuracy. Additionally, balancing model accuracy with computational efficiency—especially for real-time applications—often requires careful model selection and optimization. Collaboration with data engineers and domain experts is also typical to ensure data relevance and model applicability.

What is machine learning object detection?

Machine learning object detection is a field within artificial intelligence that focuses on identifying and locating objects within images or videos. It uses algorithms and deep learning models, such as convolutional neural networks (CNNs), to analyze visual data and predict the presence and position of various objects. Object detection is widely used in applications like autonomous vehicles, security surveillance, and image search. The process typically involves training models on labeled datasets so they can accurately detect and classify multiple objects in complex scenes.
What are popular job titles related to Machine Learning Object Detection jobs in Missouri? For Machine Learning Object Detection jobs in Missouri, the most frequently searched job titles are:

Machine Learning Engineer with Security Clearance

SecureVision

Saint Louis, MO • On-site

Other

Re-posted 25 days ago


Job description

HOW A MACHINE LEARNING ENGINEER WILL MAKE AN IMPACT
Own your opportunity to serve as a critical component of our nation's safety and security. Make an impact by using your expertise to protect our country from threats. Job Description
Rapidly prototype containerized multimodal deep learning solutions and associated data pipelines to enable GeoAI capabilities for improving analytic workflows and addressing key intelligence questions. You will be at the cutting edge of implementing State-of-the-Art (SOTA) Computer Vision (CV) and Vision Language Models (VLM) for conducting image retrieval, segmentation tasks, AI-assisted labeling, object detection, and visual question answering using geospatial datasets such as satellite and aerial imagery, full-motion video (FMV), ground photos, and OpenStreetMap.
WHAT YOU'LL NEED TO SUCCEED:
• Education: Bachelor or Master' Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
• Experience: 5+ years Technical skills:
• Demonstrated experience applying transfer learning and knowledge distillation methodologies to fine-tune pre-trained foundation and computer vision models to quickly perform segmentation and object detection tasks with limited training data using satellite imagery.
• Demonstrated professional or academic experience building secure containerized Python applications to include hardening, scanning, automating builds using CI/CD pipelines.
• Demonstrated professional or academic experience using Python to query and retrieve imagery from S3 compliant API's perform common image preprocessing such as chipping, augment, or conversion using common libraries like Boto3 and NumPy.
• Demonstrated professional or academic experience with deep learning frameworks such as PyTorch or Tensorflow to optimize convolutional neural networks (CNN) such as ResNet or U-Net for object detection or segmentation tasks using satellite imagery.
• Demonstrated professional or academic experience with version control systems such as Gitlab.
• Demonstrated experience leveraging CUDA for GPU accelerated computing. Skills and abilities desired:
• Demonstrated professional or academic experience with the HuggingFace Transformers library and hub.
• Demonstrated experience with OpenShift and container orchestration within Kubernetes using Helm, Kubectl, Kustomize, or Operators.
• Demonstrated experience with Vision Transformers (ViT) such as DINO or DeiT.
• Demonstrated academic or professional experience communicating methodological choices and model results.
• Demonstrated experience with verification and validation test benches.
• Demonstrated experience with Explainable AI (XAI) techniques.
• Demonstrated experience with Open Neural Net Exchange (ONNX).