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Contract Computer Vision Deep Learning Engineer Jobs in Saint Louis, MO

Brooksource

MLE II

Saint Louis, MO · On-site

$50 - $55/hr

... modern software engineering best practices. * Familiarity with deep learning concepts and ... Medical, Dental & Vision Plans * Relationship-Driven Process to Find Your Best Fit * 6 Paid ...

Varsity Tutors

Machine Learning Tutor

Saint Louis, MO · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

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All JobsContract Computer Vision Deep Learning Engineer JobsContract Computer Vision Deep Learning Engineer Jobs in Saint Louis, MO

Contract Computer Vision Deep Learning Engineer information

See Saint Louis, MO salary details

$47.2K

$118.1K

$133.7K

How much do contract computer vision deep learning engineer jobs pay per year?

As of Jul 31, 2026, the average yearly pay for contract computer vision deep learning engineer in Saint Louis, MO is $118,140.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,400.00 and $127,800.00 per year, depending on experience, location, and employer.
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Contract Computer Vision Deep Learning Engineer jobs near you

Machine Learning Engineer with Security Clearance

SecureVision

Saint Louis, MO • On-site

Other

Re-posted 21 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).

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