Remote AI Innovation Engineer - Scale ML Systems

Bright Vision Technologies

Chesterfield (MO)

Remote

USD 147,000 - 198,000

Full time

4 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. You will design and ship advanced ML systems that solve high-impact business problems, balancing scientific rigor with robust software engineering.

Expect to work across problem framing, experimentation, deployment, and continuous improvement. The ideal candidate has deep ML expertise, experience translating research into scalable production pipelines, and strong

Qualifications

  • Master’s or PhD in Computer Science, ML, Statistics, or closely related field, or equivalent applied experience.
  • Six+ 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.
  • Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML.
  • Experience taking ML models from research prototype to production with observability and safeguards.
  • Familiarity with distributed training, mixed-precision training, and accelerator hardware.
  • Strong written and verbal communication skills, including ability to explain complex methods clearly.
  • Demonstrated ability to read, evaluate, and adapt techniques from current research literature.
  • Track record of shipping impactful applied AI projects.

Skills

Python
ML frameworks
Distributed training
Communication

Education

Master’s or PhD in CS/ML

Tools

PyTorch
JAX

Job description

Bright Vision Technologies is seeking an AI Innovation Engineer to bridge cutting-edge applied research and production engineering. You will design and ship advanced ML systems that solve high-impact business problems, balancing scientific rigor with robust software engineering.

Expect to work across problem framing, experimentation, deployment, and continuous improvement. The ideal candidate has deep ML expertise, experience translating research into scalable production pipelines, and strong

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