Senior AI Innovation Engineer (Remote)

Bright Vision Technologies

United States

Remote

USD 135,000 - 210,000

Full time

6 days ago
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Job summary

Bright Vision Technologies is seeking an AI Innovation Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems.

The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deployment-ready pipelines, with the ability to operate across the full lifecycle from experimentation to continuous improvement.

Qualifications

  • Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field, or equivalent applied experience.
  • Six or more years of combined research and applied ML engineering experience.
  • Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX.
  • Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.

Responsibilities

  • Bridge cutting-edge applied research and production engineering.
  • Design and ship advanced machine learning systems that solve high-impact business problems.
  • Balance scientific rigor with pragmatic software engineering and robust production pipelines.
  • Lead lifecycle from problem framing and experimentation to deployment and continuous improvement.

Skills

Python
PyTorch
JAX
Deep learning
MLOps
Distributed training
Observability
Production deployment
Communication

Education

Master’s or PhD in CS/ML/Stat or related field

Job description

Bright Vision Technologies is seeking an AI Innovation Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems.

The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deployment-ready pipelines, with the ability to operate across the full lifecycle from experimentation to continuous improvement.

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