Cagri Tanriover

I’m a senior R&D engineer with 20+ years of experience converting complex ideas into working systems that ship. I work end-to-end—firmware and data capture, Python/ML pipelines, and stakeholder alignment—to deliver measurable business outcomes. I’ve shipped code to 600+ devices, filed 26 patents, mentored 400+ engineers, and advised companies on strategy and execution.

My recent focus is sensing-driven products and data/ML at scale. At Intel (Hillsboro), I created two large, real-world datasets—an RFID set with 1,000 tagged items and a radar gesture set with 600 labeled instances—to accelerate downstream ML for internal teams and external customers. I also led work that removed a 3D-camera dependency by deploying a deep-learning computer-vision pipeline in Python (saving ~$300 per unit), and cut training-data needs by ~30% with sparse point-cloud selection—without sacrificing accuracy.

Beyond hands-on engineering, I’m comfortable driving roadmaps and cross-functional delivery: advising management on research strategy, aligning with product teams and external OEM partners (e.g., Dell, Lenovo, HP), managing EU Horizon and university collaborations, screening/interviewing candidates, and documenting decisions for reproducibility. I also designed a patent coaching program that mentored 400+ engineers and reviewed 1,700+ disclosures, strengthening IP strategy across the org.

Previously in Istanbul, I led wearable prototypes and partner engagements, established an Ignition Lab to speed collaboration, built a foundational Intel Edison breakout board for the wearables program, engineered the interactive “Butterfly Dress” (featured among Intel’s Top 10 innovations and in the Intel Brand Book), and delivered a smart-wearable proof of concept for cerebral palsy monitoring in 3 months. These projects reflect my bias for rapid prototyping and real-world validation.

Core strengths: Python (NumPy, pandas, scikit-learn), embedded/firmware to cloud data capture, mmWave/Wi-Fi/RFID sensing, feature engineering and experiment design, project management, field deployments, stakeholder communication, hiring/interviews, and clear documentation. I enjoy tackling ambiguous problems, instrumenting the right data, and building reliable systems that move from prototype to production.

If you’re looking for someone who can bridge hardware, data, and ML—and show impact with concrete metrics—I can help.