PhD Applied Research Intern
$104,000–$104,000 year
Remote · United States
Job Summary
PhD Applied Research Intern on Centific’s Vision AI team for a 3–6 month engagement. Embedded in a live AI operations program to translate model research into working, measurable systems that process real-time video feeds across multiple detection workflows. You will contribute to post-run analysis, monitor pipeline KPIs, and be assigned to two of five focus tracks (NVIDIA DeepStream optimization; Teacher→Student distillation; SEAL drift detection & auto-correction; self-distillation & confidence calibration; student↔student weighted peer learning). The role requires a PhD candidate with strong computer vision, Python, and DL framework skills, experience with large-scale data processing and annotation workflows, and the ability to deploy research concepts into production environments. Possesses hands-on experience with model training, transfer learning, distillation, and calibration techniques; familiarity with MLOps tools and cloud platforms is a plus. The position is remote-first with hybrid options, offering hands-on ownership of a live surveillance AI system, mentorship from senior ML engineers, and potential for full-time opportunities after PhD completion.
Required Qualifications
- Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field
- Strong orientation toward applied systems and implementation
- Deep expertise in computer vision fundamentals: convolutional neural networks, transformers (ViT, DETR), and generative models
- Strong proficiency in Python and deep learning frameworks including PyTorch and/or TensorFlow
- Hands-on experience with large-scale dataset processing, annotation workflows, or benchmark construction
- Solid understanding of model training techniques: transfer learning, self-supervised learning, and fine-tuning strategies
- Strong implementation skills: ability to take a model research concept and produce a working, measurable system quickly
- Clear written and verbal communication skills; ability to document implementation decisions, pipeline changes, and performance results for both technical and operational audiences
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