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CaseGuard

Machine Learning Engineer - Computer Vision

Arlington, Virginia, United States

Location
Arlington, Virginia, United States
Work
Not stated
Employment
Not stated
Level
Not stated
Function
Data & AI
Pay
Not stated
Posted
8 months ago

Pay and the market here

61 data jobs open in Arlington: 18% remote, 12 posted in the last 30 days.

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About the role

We are seeking a highly skilled and motivated Machine Learning Engineer specializing in Computer Vision to join our team. The ideal candidate will have a strong background in developing and deploying machine learning models focused on image and video processing. You will work closely with cross-functional teams to design, implement, and optimize vision-based AI solutions to address real-world challenges.

Key Responsibilities

  • Design, develop, and deploy computer vision models for tasks such as object detection, object tracking, video segmentation, and facial recognition.
  • Optimize and fine-tune deep learning algorithms for real-time performance.
  • Work closely with the software engineers and product teams to identify opportunities for leveraging data.
  • Collect, clean, and preprocess large datasets to prepare for model training and evaluation.
  • Evaluate and optimize machine learning models for accuracy, performance, and scalability.
  • Deploy models into production environments and monitor their performance to ensure reliability.
  • Stay up-to-date with the latest advancements in computer vision and artificial intelligence.
  • Collaborate with cross-functional teams to integrate machine learning solutions into business processes.
  • Document processes, models, and implementations to ensure reproducibility and scalability.

Required Qualifications

  • Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • Experience in deep learning models, their training, and hyperparameter tuning using libraries such as TensorFlow, PyTorch, and Transformers or other Huggingface tools.
  • Experience with data manipulation tools such as Pandas, NumPy, and SQL.
  • Strong programming skills in Python and C++.
  • Experience in MLOps principles and model deployment and instrumentation on cloud platforms such as AWS, Azure, or Google Cloud for model deployment and knowledge with efficient serving tools such as ONNX, triton, and vllm.
  • Proficiency in working with image and video data, including preprocessing and augmentation techniques.
  • Strong understanding of machine learning algorithms, including supervised and unsupervised learning and deep learning.
  • Strong communication skills and the ability to work collaboratively in a team environment.

Great to have

  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Experience with version control systems such as Git.
  • Understanding software engineering best practices, including code review, testing, and documentation.
  • Experience with Large Language Models (LLMs) is a great plus.
  • Experience with data annotation tools and processes.

Benefits

  • Competitive Salary
  • Stock Option
  • Medical, Dental, and Vision Insurance
  • 401K
  • Paid Vacation
  • Ten paid holidays per year
  • Friendly and Learning environment

About CaseGuard

CaseGuard is a software company that helps law enforcement agencies, federal agencies, hospitals, schools, airports, and others manage all their media redaction needs in one easy-to-use redaction software. CaseGuard Studio is one of a kind. Our team is driven by a passion for great software design, creating great products, and creative processes; CaseGuard implements innovative ideas across multiple services and agencies. We invest in people. We nurture skills consistent with our values and our future strategy. Our passionate pursuit of excellence, the application of our creativity to solve our clients’ challenges, our technical expertise, and our collaborative spirit are measures of our success.

Source

Posted by CaseGuard on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 11:47 UTC; the original is the reference.

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