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Centific

Unlock the Value of AI and Unleash the Possibilities

PhD Applied Research Intern

Research EngineerResearch EngineerInternshipRemoteEntry LevelTeam 5,001-10,000H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

7 days ago

Salary

$50 / hour

Seniority

Entry Level

Job Description

PhD Applied Research Intern

Centific

Role Description 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. Key Responsibilities - Monitor daily pipeline KPIs. - Contribute to post-run analysis. - Document implementation decisions in the team ops ledger. - Assigned to two of the following five focus tracks for the duration of the engagement: - Track 1 — NVIDIA VSS / DeepStream Optimization: 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. - Track 2 — Teacher → Student Distillation: 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. - Track 3 — SEAL Drift Detection & Auto-Correction: 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. - Track 4 — Self-Distillation & Confidence Calibration: 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. - Track 5 — Student ↔ Student Weighted Peer Learning: 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. Qualifications - 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. Preferred Qualifications - 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). What You Will Gain - 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. Compensation & Logistics - Compensation: Competitive hourly stipend commensurate with PhD program year and experience. - Location: Remote-first; hybrid options available at select office locations. - Start Date: Flexible — rolling admissions, positions filled as qualified candidates are identified. - Duration: 3–6 months, with possibility of extension. - Equipment: Laptop and cloud compute credits provided. - Rate: $50 per hr. Centific is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.

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DTEX Systems logo

Threat Intel Research Engineer

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Powering a trusted workforce by stopping insider risks from becoming insider threats. #IRM #DLP #UBA #UAM

Full TimeRemoteTeam 51-200Since 2002H1B Sponsor

• Design and implement behavioral detections and analytic content using DTEX’s proprietary telemetry and data models. • Build and maintain full-stack applications and internal tools that support intelligence workflows, detection pipelines, and customer-facing features. • Develop and optimize algorithms for behavioral modeling, anomaly detection, and risk scoring using structured and unstructured data. • Lead QE efforts for the Intelligence team, including test automation, regression validation, and release readiness for detection content and supporting tools. • Partner with data scientists, detection engineers, product managers, and customer success teams to translate intelligence needs into scalable solutions. • Stay current with emerging threats, detection methodologies, and full-stack technologies to continuously improve DTEX’s detection and response capabilities.

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