Ghaith Ale-Lead Perception Engineer
Check rate
Experience
Lead Perception Engineer
Driving Examiner AI Platform
- Automated driver assessment by programming temporal rule engines to evaluate lane-change execution safety, head-pose mirror checks, indicator usage cycles, and compliance with traffic lights and road signs
- Synchronized real-time traffic sign recognition and multi-state traffic light classification models with time-series CAN-bus telemetry and HD-map spatial priors to grade traffic rule adherence
- Trained and deployed distinct deep learning models optimized for interior cabin monitoring and exterior surrounding-area perception
- Combined perception outputs with camera intrinsics and horizon stability checks to execute 3D ground-plane object distance estimation assuming flat-ground geometry
- Deployed a split-compute edge network across a 10-vehicle fleet via VPN, implementing a zero-allocation host memory pipeline to eliminate frame accumulation latency (6×21 FPS per vehicle)
Independent AI Contractor
Independent AI Contractor
- Developed a low-overhead, multi-threaded C++ ONNX benchmarking framework to evaluate models and streamline ONNX Runtime deployment workflows
- Programmed initialization routines to query model metadata and bind available execution providers (CPU, CUDA, TensorRT)
- Built benchmarking workflows supporting deterministic latency evaluation for single-image inference and live video streams
- Implemented asynchronous producer-consumer threading loops and pinned-memory allocation schemas to reduce cache misses and I/O decoding bottlenecks
- Built an on-device Android privacy application using a custom segmentation model to achieve 27+ FPS on low-power mobile CPUs via XNNPACK acceleration and frame buffers
- Architected an interactive semi-automated annotation suite combining SAM 2 point-prompting with YOLO segmentation for active learning dataset curation
- Engineered asynchronous initialization mechanisms to automatically pre-annotate target frames via custom segmentation networks
- Integrated polygon geometry masks to isolate road scene regions and remove window reflections and implemented linear pixel transformations for mapping zoom-space mouse inputs
- Programmed serialization pipelines using contour retrieval algorithms to map memory masks to normalized YOLO polygon arrays
- Engineered a multi-camera intrusion detection framework handling 60+ concurrent video streams with custom restricted regions of interest per camera
- Built a cascaded detection pipeline for high-resolution imagery by first detecting macro targets, cropping regions of interest, and passing lower-resolution cropped regions into fine-grained models to reduce VRAM usage and prevent out-of-memory errors
Industry experience
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Experienced in Information Technology and Automotive.
Business area experience
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Experienced in Product Development, Research and Development, and Information Technology.
Summary
Motivated by a strong curiosity for how machines process spatial and visual data, currently pursuing an M.Sc. in Artificial Intelligence. Core focus centers on the complete development cycle of computer vision systems—from building custom data engineering tools and training deep learning models to writing low-overhead C++ inference utilities. Fascinated by the challenge of taking networks out of isolated cloud environments and optimizing pipelines to run fluidly alongside real-world vehicle telemetry on resource-constrained edge hardware.
Skills
Core Languages: C++, Python
Spatial Vision & Mathematics: 3d Coordinate Transformations, Projective Geometry, Matrix Transformations, Camera Calibration (Intrinsics/Extrinsics), Monocular Depth / Geometric Ground-Plane Modeling
Deep Learning & Cv Frameworks: Pytorch, Object Detection (Standard & Oriented Bounding Boxes), Segmentation (Semantic, Instance), Pose Estimation, Segment Anything Model (Sam 2), Yolo Ecosystem
Inference Optimization & Deployment: Onnx Runtime (Graph Reflection, Provider Auditing), Tensorrt (Fp16/Quantization Engine), Xnnpack Hardware Acceleration, Pinned-Memory Architecture, Model Profiling
Systems & Hardware Ecosystems: Ros, Multi-Threading (Producer-Consumer), Shared Memory Systems, Linux Administration, Docker, Git/Gitlab Ci/Cd, Vpn Networks, Nvidia Jetson Platform (Nano/Orin), Edge Soc Implementations
Languages
Education
Brandenburgische Technische Universität Cottbus-Senftenberg (BTU)
M.Sc. Artificial Intelligence (Research-oriented) · Artificial Intelligence · Cottbus, Germany
Tishreen University
Integrated Master’s (B.Sc. & M.Sc. Equivalent) (AI Focus) · Artificial Intelligence · Latakia, Syrian Arab Republic
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
Ghaith is based in Cottbus, Germany.
Ghaith speaks the following languages: Arabic (Native), English (Advanced), German (Elementary).
Ghaith has at least 4 years of experience. During this time, Ghaith has worked in at least 2 different roles and for 2 different companies. The average length of individual experience is 2 years and 2 months. Note that Ghaith may not have shared all experience and actually has more experience.
Based on recent experience, Ghaith would be well-suited for roles such as: Lead Perception Engineer, Independent AI Contractor.
Ghaith's most recent position is Lead Perception Engineer at Driving Examiner AI Platform.
In recent years, Ghaith has worked for Driving Examiner AI Platform and Independent AI Contractor.
Ghaith is most experienced in industries like Information Technology and Automotive.
Ghaith is most experienced in business areas like Product Development, Research and Development, and Information Technology.
Ghaith holds a Master in Artificial Intelligence from Brandenburgische Technische Universität Cottbus-Senftenberg (BTU) and a Master in Artificial Intelligence from Tishreen University.
Ghaith is immediately available full-time for suitable projects.
Daily rate distribution
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Average rates for similar positions
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The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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