Afaq Afaq Saeed - Master’s Thesis Researcher – Multiview Perception Evaluation

Q: Where is Afaq based?

Afaq is based in Wolfsburg, Germany .

Q: What languages does Afaq speak?

Afaq speaks the following languages: Urdu (Native), English (Advanced), German (Intermediate) .

Q: How many years of experience does Afaq have?

Afaq has at least 5 years of experience. During this time, Afaq has worked in at least 5 different roles and for 5 different companies . The average length of individual experience is 1 year . Note that Afaq may not have shared all experience and actually has more experience.

Q: What is Afaq's latest experience?

Afaq's most recent position is Master’s Thesis Researcher – Multiview Perception Evaluation at Volkswagen AG .

Q: What companies has Afaq worked for in recent years?

In recent years, Afaq has worked for Volkswagen AG , NavVis GmbH , Fraunhofer IIS , Sayraab , and RoadGauge AI / Strada Imaging .

Q: Which industries is Afaq most experienced in?

Afaq is most experienced in industries like Information Technology , Construction , and Professional Services . Afaq also has some experience in Automotive .

Q: Which business areas is Afaq most experienced in?

Afaq is most experienced in business areas like Research and Development , Product Development , and Information Technology . Afaq also has some experience in Quality Assurance .

Q: What is Afaq's education?

Afaq holds a Master in Artificial Intelligence from Friedrich-Alexander-Universität Erlangen-Nürnberg and a Bachelor in Mechatronics Engineering from National University of Sciences and Technology .

Q: What is the availability of Afaq?

Afaq will be available full-time from July 2026 .

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Wolfsburg, Germany

Check rate

Experience

May 2025 - Feb 2026

10 months

Wolfsburg, Germany

Master’s Thesis Researcher – Multiview Perception Evaluation

Volkswagen AG

Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.

Dec 2024 - May 2025

6 months

Erlangen, Germany

Working Student – Mapping & Robotic Perception

NavVis GmbH

Developed image- and point-cloud-based algorithms for detecting and localizing reference markers in mobile-mapping data from real sensor systems.
Built an RGB–LiDAR pipeline using image-based candidate selection, point-cloud filtering, plane estimation, clustering, coordinate transformations, and geometric center recovery.
Evaluated approximately 20 test mappings, achieving around 2.5 mm marker-center localization error and locating approximately 85% of target markers in the prototype workflow.
Debugged sensor-data and geometry issues, created VTK-based visualization tools, and documented test scenarios, accuracy results, and recurring failure cases.

Dec 2023 - Nov 2024

1 year

Erlangen, Germany

Working Student – Multimodal 3D Reconstruction

Fraunhofer IIS

Investigated vision- and audio-based methods for estimating indoor geometry, comparing their robustness across seven real environments.
Built photogrammetry and NeRF reconstructions and aligned them with audio-derived room estimates for multimodal spatial reasoning.
Analyzed sensor-specific limitations, including visually difficult glass surfaces and acoustically absorbing structures, to identify complementary fusion strategies.
Trained and evaluated learning-based models on HPC systems and presented experimental findings through technical posters, prototypes, and documentation.

Jan 2023 - Nov 2023

11 months

Germany

MLOps Consultant – ML Platform Engineering

Sayraab

Supported a scalable containerized ML environment with authentication, resource quotas, monitoring, reproducible workspaces, and automated deployment workflows.
Worked across software, infrastructure, and user requirements to improve reliability and operational structure for machine-learning experimentation.

Dec 2020 - Oct 2023

2 years 11 months

United Kingdom

CV/ML Intern → Engineer → Team Lead Perception

RoadGauge AI / Strada Imaging

Progressed from intern to engineer and team lead while developing AI-powered road-inspection and asset-mapping systems operating on uncontrolled outdoor imagery.
Designed end-to-end pipelines for semantic segmentation, object detection, tracking, traffic-sign recognition, stereo/SfM reconstruction, pavement measurement, and spatial asset localization.
Contributed to systems used across approximately 5,000 km of road inspection, reducing manual review effort by up to 90% and supporting three major clients.
Integrated stereo-vision software with custom camera hardware and Raspberry Pi edge computers, addressing calibration, synchronization, performance, and deployment constraints.
Led six perception engineers and coordinated ten data annotators, covering architecture decisions, technical reviews, dataset quality, validation, debugging, and delivery planning.

Industry Experience

See where this freelancer has spent most of their professional time.

Experienced in Information Technology, Construction, Automotive, and Professional Services.

Information Technology

Construction

Automotive

Professional Services

Business Area Experience

See which departments and functions this freelancer has contributed to most.

Experienced in Research and Development, Product Development, Information Technology, and Quality Assurance.

Research and Development

Product Development

Information Technology

Quality Assurance

Summary

AI and robotics engineer with 4+ years of professional experience developing computer-vision, machine-learning, and 3D perception systems for mobile mapping, infrastructure inspection, autonomous-driving research, and robotic prototypes. Combines an M.Sc. in Artificial Intelligence with a mechatronics background spanning sensors, embedded systems, control, robot hardware, and software integration. Experienced in Python, C++, PyTorch, ROS/ROS2, RGB–LiDAR processing, point clouds, stereo vision, sensor-data evaluation, edge deployment, debugging, and technical leadership for real-world systems.

Skills

Robotics & Autonomous Systems: Ros/Ros2, Nav2, Mobile Robotics, Localization, Mapping, Navigation, Sensor Integration, Coordinate Transformations, Robot Perception, Control Fundamentals, Real-Sensor Testing.
Ai & Computer Vision: Pytorch, Tensorflow/Keras, Opencv, Open3d, Pcl, Detection, Semantic Segmentation, Tracking, Stereo Vision, Sfm, Nerf, Lidar Processing, Multimodal And Generative-Model Evaluation.
Software & Deployment: Python, C++ Working Proficiency, Matlab, Linux, Git, Docker, Cuda, Gcp, Azure Ml, Raspberry Pi, Testing, Benchmarking, Failure Analysis, Performance Evaluation, Technical Documentation.

Languages

Urdu

Native

English

Advanced

German

Intermediate

Education

Oct 2023 - Mar 2026

Friedrich-Alexander-Universität Erlangen-Nürnberg

M.Sc. Artificial Intelligence · Artificial Intelligence · Erlangen, Germany · 1.7

Sep 2017 - Jul 2021

National University of Sciences and Technology

Bachelor of Mechatronics Engineering · Mechatronics Engineering · Pakistan · 3.63/4.00

Statistics

Experience

Total positions 5

Experience in Information Technology 3.5 y

Avg length 1 y 2 m

Longest experience 2 y 10 m

Global Experience

Countries worked in 2 (Germany, United Kingdom)

Primary country Germany

Expertise

Recent roles Master’s Thesis Researcher – Multiview Perception Evaluation, Working Student – Mapping & Robotic Perception, Working Student – Multimodal 3D Reconstruction

Main industries Information Technology, Construction, Automotive

Main business areas Research and Development, Product Development, Information Technology

Qualifications

Highest degree Master

Profile

Created

June 2026

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Frequently asked questions

Have questions? Find more information here.

Where is Afaq based?

Afaq is based in Wolfsburg, Germany.

What languages does Afaq speak?

Afaq speaks the following languages: Urdu (Native), English (Advanced), German (Intermediate).

How many years of experience does Afaq have?

Afaq has at least 5 years of experience. During this time, Afaq has worked in at least 5 different roles and for 5 different companies. The average length of individual experience is 1 year. Note that Afaq may not have shared all experience and actually has more experience.

What roles would Afaq be best suited for?

Based on recent experience, Afaq would be well-suited for roles such as: Master’s Thesis Researcher – Multiview Perception Evaluation, Working Student – Mapping & Robotic Perception, Working Student – Multimodal 3D Reconstruction.

What is Afaq's latest experience?

Afaq's most recent position is Master’s Thesis Researcher – Multiview Perception Evaluation at Volkswagen AG.

What companies has Afaq worked for in recent years?

In recent years, Afaq has worked for Volkswagen AG, NavVis GmbH, Fraunhofer IIS, Sayraab, and RoadGauge AI / Strada Imaging.

Which industries is Afaq most experienced in?

Afaq is most experienced in industries like Information Technology, Construction, and Professional Services. Afaq also has some experience in Automotive.

Which business areas is Afaq most experienced in?

Afaq is most experienced in business areas like Research and Development, Product Development, and Information Technology. Afaq also has some experience in Quality Assurance.

What is Afaq's education?

Afaq holds a Master in Artificial Intelligence from Friedrich-Alexander-Universität Erlangen-Nürnberg and a Bachelor in Mechatronics Engineering from National University of Sciences and Technology.

What is the availability of Afaq?

Afaq will be available full-time from July 2026.

What is the rate of Afaq?

Afaq's rate depends on the specific project requirements. Please use the Meet button on the profile to schedule a meeting and discuss the details.