Remote AI Research Engineer for Scalable ML

Bright Vision Technologies; SOUTH PLAINFIELD (NJ); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Research Engineer to bridge cutting-edge research and production engineering. You will design and ship advanced machine learning systems addressing high-impact business problems with robust pipelines and observability.

The role requires deep ML knowledge, modern framework experience, and the ability to influence end-to-end AI solutions from problem framing to deployment and continuous improvement.

Qualifications

• Master’s or PhD in Computer Science, ML, Statistics, or related field, or equivalent applied experience.
• 12+ years of combined research and ML engineering.
• Strong Python and ML framework skills (PyTorch/JAX).
• Experience training, fine-tuning, evaluating DL models at scale.
• Experience taking ML models from research to production with observability.

Responsibilities

• Design, prototype, and evaluate AI solutions across NLP, vision, recommender, and data domains.
• Translate business problems into ML formulations with clear metrics.
• Stay current with deep learning and LLM research and assess applicability.
• Build production-oriented training and inference pipelines with robust observability.
• Collaborate with platform engineers to optimize compute and storage usage.
• Develop tooling for dataset creation, labeling, validation, and monitoring.

Skills

Python

PyTorch

JAX

Distributed training

Deep learning

Experimentation

Communication

Research to production

Education

Master's or PhD in CS/ML/Statistics

Tools

Git

MLflow

TensorBoard

Job description

Bright Vision Technologies is seeking an AI Research Engineer to bridge cutting-edge research and production engineering. You will design and ship advanced machine learning systems addressing high-impact business problems with robust pipelines and observability.

The role requires deep ML knowledge, modern framework experience, and the ability to influence end-to-end AI solutions from problem framing to deployment and continuous improvement.