Applied Vision Scientist - ML & Production at Scale

ARRISE

United States

On-site

USD 120,000 - 190,000

Full time

14 days+
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Job summary

ARRISE is seeking a Data Scientist to join the applied-research end of computer vision. You will frame open-ended problems as hypotheses, develop prototypes, and choose model architectures best suited to each task. Computer vision is the core domain with scope across text, audio, and tabular data. You will help take validated prototypes into production and see them run at scale.

Even if you don’t meet every requirement, your ability to deliver impact matters most.

Qualifications

  • Master’s degree in a quantitative field (e.g., EE/CS) and hands-on ML experience.
  • Demonstrated computer vision work (object detection, classification, tracking).
  • Strong Python skills and experience with DL frameworks (PyTorch or TensorFlow).
  • Experience with large, complex datasets and robust experimentation.

Responsibilities

  • Design and prototype ML and deep learning models with computer vision as the primary domain.
  • Frame business problems as testable hypotheses and develop proofs-of-concept.
  • Evaluate and benchmark model architectures and pre-trained models.
  • Collaborate with ML Engineers to scale validated prototypes into production.
  • Track experiments, data/versioning, and define evaluation metrics.
  • Work with product/engineering/business teams to apply ML solutions.

Skills

Computer vision
Machine learning
Python
Experimentation
Team collaboration

Education

Master's degree in Electrical Engineering, Computer Science, or related field

Tools

PyTorch
TensorFlow
Docker
Hugging Face Transformers

Job description

ARRISE is seeking a Data Scientist to join the applied-research end of computer vision. You will frame open-ended problems as hypotheses, develop prototypes, and choose model architectures best suited to each task. Computer vision is the core domain with scope across text, audio, and tabular data. You will help take validated prototypes into production and see them run at scale.

Even if you don’t meet every requirement, your ability to deliver impact matters most.

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