PhD Applied Research Intern

Centific

1d • $50 • Remote

About The Position

We are seeking a highly motivated PhD intern to join Centific’s Vision AI team for a 3–6 month engagement. This is an applied research role for doctoral candidates who want to move beyond the lab and deploy their expertise directly into a live AI operations program. You will be embedded in a production computer vision system processing real-time video feeds across multiple active detection workflows. You will work alongside senior engineers and ML leads to implement, optimize, and measure AI improvement strategies that ship to production on a daily pipeline cadence. The emphasis is on building and shipping: translating model research into working, measurable systems that improve real-world detection performance.

Requirements

• Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field, with a 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; comfort operating in a daily-cadence production pipeline environment.
• Clear written and verbal communication skills; ability to document implementation decisions, pipeline changes, and performance results for both technical and operational audiences.

Nice To Haves

• Hands-on experience with NVIDIA DeepStream, TensorRT, or TAO Toolkit; familiarity with RTSP stream processing, multi-stream batching, or edge inference optimization.
• Familiarity with 3D vision, point cloud processing, or LiDAR-visual fusion (particularly in outdoor surveillance or autonomous systems contexts).
• Practical experience with knowledge distillation (Teacher → Student, self-distillation, or peer learning), confidence calibration techniques (temperature scaling, isotonic regression, ECE measurement), or active learning / distribution shift detection.
• Prior industry internship experience in AI/ML research or data-centric AI.
• Prior experience contributing to a production AI pipeline or daily model training cadence; comfort reading and interpreting confusion matrices, F1/Precision/Recall trends, and confidence interval dashboards as operational signals.
• Experience with MLOps tools (Weights & Biases, MLflow, DVC) and cloud platforms (AWS, GCP, or Azure).

Responsibilities

• Monitor daily pipeline KPIs, contribute to post-run analysis, and document implementation decisions in the team ops ledger.
• Optimize real-time RTSP feed processing and multi-stream batching; configure and tune object tracking to eliminate re-detection false positives and reduce hallucination rates across active surveillance workflows.
• Implement and run distillation cycles that compress 20+ epoch full retrains into 3-epoch student passes; maintain and improve three student model variants with daily pipeline integration and performance validation.
• Monitor metrics against a rolling baseline to detect distribution shift; execute targeted fine-tuning or short retraining cycles when drift thresholds are crossed, and systematically reduce recurring false positives.
• Run self-distillation refinement passes where student models act as their own teachers; apply consistency confidence calibration to narrow confidence intervals, and reduce overconfidence-driven hallucinations.
• Run confidence-weighted ensemble computations across three student model variants, monitor inter-student disagreement rates, route high-disagreement frames to the human review queue, and conduct weekly contribution audits to ensure balanced peer learning and prevent teacher-bias propagation.

Benefits

• Hands-on ownership of a live, production AI system processing real-world surveillance data daily — with measurable KPI targets, real drift events, and deployment decisions that matter.
• Mentorship from senior ML engineers and AI leads with deep expertise in deployed Vision AI systems, model distillation, drift correction, and edge inference optimization.
• Direct contribution to measurable performance improvements — reductions in hallucination rate, narrowing of confidence intervals, and F1 score gains — on a live public safety AI program.
• Access to proprietary datasets, annotation infrastructure, and compute resources for research experiments.
• Attribution and credit in Centific’s IP Vault for implemented strategies and methodology contributions, with potential for technical blog posts, internal white papers, or co-authorship on applied research artifacts arising from the program.
• Consideration for full-time opportunities upon PhD completion based on performance.
• Competitive hourly stipend commensurate with PhD program year and experience
• Laptop and cloud compute credits provided

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree