Senior Deep Learning Algorithm Researcher/Engineer(Seoul)

[About STRADVISION] 

We Empower Everything To Perceive Intelligently 

With a mission statement of “We Empower Everything To Perceive Intelligently”, STRADVISION is putting all of our effort to make better life for everyone through AI-based camera perception technology. Everyday, we focus on creating AI-based vision perception techonolgy with more than 300 members across 8 offices worldwide and we expect our software to perceive everything precisely & intelligently to make 1% difference in people’s lives. Thus, we are looking for members who would like to join our meaningful journey and face challenges that no one has done it before together at STRADVISION.      

[Our Story] 

• STRADVISION Seoul Office Intro Video: 스트라드비젼 서울 강남 오피스를 소개합니다!     
• STRADVISION Re-branding PR Video: 스트라드비젼 리브랜딩 PR 홍보영상 

[Our Technology] 

• STRADVISION is the FIRST deep-learning based technology start-up company in the world who has obtained ASPICE CL2 certification in 2019.
• STRADVISION has also been honored with the AutoSens Awards for ‘Best-in-Class Software for Perception Systems’(Gold Award Winner) for 2 years in a row(2021, 2022).   
• STRADVISION’s outstanding technology was recognized worldwide by successfully completing the Series C funding at KRW 107.6 billion with Aptiv and ZF Group in August, 2022.
• About 167 patents related to autonomous driving/ADAS have been acquired in Korea, Japan, US and Europe. As of today, STRADVISION is actively developing our technology to be differentiated. 
• STRADVISION Product: https://stradvision.com/sv/en/product 

[Mission of the Role] 

As a Senior Deep Learning Researcher/Engineer, your mission is to develop and optimize multi-camera-based vision perception and deep learning-driven path planning systems that enable safe, robust, and high-performance autonomous driving at ADAS Level 2++ and beyond.

You will contribute by:

• Enhancing multi-camera-based perception systems to achieve human-like environmental understanding, even in complex driving conditions.
• Developing high-precision path planning algorithms that ensure smooth, safe, and efficient navigation under dynamic road scenarios.
• Leveraging state-of-the-art deep learning and AI techniques such as Vision Transformers, Graph Neural Networks (GNNs), and Reinforcement Learning (RL) to advance autonomous vehicle intelligence.
• Bridging the gap between research and real-world deployment by optimizing models for real-time, low-latency inference on edge computing platforms.
• Ensuring generalization and robustness of perception and planning algorithms across diverse driving environments, including adverse weather and dense urban traffic.

This role is a unique opportunity to work on high-impact, cutting-edge research that directly contributes to the development of next-generation autonomous driving systems.

[Key Responsibilities]

The selected candidate will be responsible for designing, developing, and optimizing deep learning models for multi-camera vision perception and path planning in ADAS Level 2++ and above autonomous vehicles.

• Vision Perception Responsibilities: 
• Develop multi-camera fusion algorithms for high-accuracy 3D object detection, lane detection and environmental understanding.
• Design and optimize Bird’s Eye View (BEV) detection, semantic segmentation, and depth estimation models.
• Implement state-of-the-art Vision Transformers (ViTs), Hybrid Transformer-CNN architectures, and Vision-Language Models (VLMs) for perception tasks.
• Enhance distance estimation and 3D scene reconstruction through multi-camera self-supervised learning.
• Improve perception system robustness against edge cases (adverse weather, occlusions, nighttime scenarios).
• Optimize an algorithm and collaborate with cross-functional teams to ensure real-time performance on an embedded device under varyingconditions.
• Path Planning Responsibilities:
• Develop deep learning-based path planning algorithms, including imitation learning, reinforcement learning (RL), and graph-based trajectory optimization.
• Implement prediction models for behavioral forecasting (e.g., pedestrian and vehicle trajectory prediction).
• Optimize end-to-end deep learning models for motion planning, integrating perception and control.
• Enhance spatiotemporal modeling for traffic scene understanding using transformers and graph neural networks (GNNs).
• Implement multi-agent reinforcement learning (MARL) for interactive decision-making in dynamic environments.
• Collaboration:
• Collaborate with cross-functional teams, including machine learning engineers, software integration engineers, hardware platform engineers, and quality assurance, to integrate camera pose estimation algorithms into ADAS systems.
• Participate in code reviews and knowledge-sharing sessions to foster a collaborative work environment.
• Mentoring and Technical Guidance:
• Mentor and provide technical guidance to junior/entry engineers.

[Basic Qualifications] 

• Ph.D. in Computer Vision, Deep Learning, Robotics, Autonomous Systems, Electrical Engineering, or related fields
• Master’s degree with 5–7 years of industry experience in deep learning-based perception or path planning for ADAS/Autonomous Driving.       
• Deep Learning & Computer Vision
• Strong knowledge of Vision Transformers (ViTs), Swin Transformers, and Hybrid CNN-Transformer models.
• Expertise in multi-camera perception, depth estimation, semantic segmentation, and 3D object detection. 
• Experience with multi-camera-to-BEV transformation, NeRF (Neural Radiance Fields), and self-supervised learning.  
• Proficiency in Vision-Language Models (VLMs) (e.g., CLIP, BLIP) for scene understanding.
• Strong background in sensor fusion techniques (camera, LiDAR, radar fusion).
• Deep Learning-based Path Planning & Prediction
• Experience in Graph Neural Networks (GNNs) and Transformers for trajectory prediction.
• Expertise in Reinforcement Learning (RL), Imitation Learning, and Model Predictive Control (MPC) for path planning.
• Understanding of Inverse Reinforcement Learning (IRL), Safe RL, and Deep Q-Learning for decision-making.
• Proficiency in Bayesian decision-making models and probabilistic motion planning techniques.
• Programming & Deployment
• Strong coding skills in Python and C++.
• Experience with deep learning frameworks (TensorFlow, PyTorch, JAX).
• Optimization skills using CUDA, TensorRT, ONNX, and multi-threaded computing.  
• Experience with real-time inference deployment on embedded platforms (NVIDIA Jetson, Orin, Xavier, Qualcomm Snapdragon, etc.).
• Knowledge of robotics middleware (ROS, ROS2) and simulation environments (CARLA, AirSim, LGSVL, etc.).
• Mathematical & Algorithm Foundations
• Strong background in optimization algorithms (gradient-based & gradient-free methods).
• Solid understanding of probabilistic models (Bayesian Networks, Kalman Filters, Hidden Markov Models).
• Proficiency in differentiable rendering and neural scene representation (NeRF, Gaussian Splatting).

[Preferred Qualifications] 

• Experience with multi-modal sensor fusion (camera + LiDAR + radar) in autonomous driving.
• Contributions to top-tier AI/Computer Vision conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML).
• Knowledge of vision transformer, vision language model, self-supervised learning, continual learning, and meta-learning techniques.
• Publications in reinforcement learning, motion planning, or self-driving perception.

[Application] 

• Required: Resume /Thesis (for those who have a Master’s degree or above.)
• Optional: Cover Letter/Project details/ Other theses   

[Recruitment Process]  

• Application Review – SC Test - Recruiter Phone Screening - Interview(s) – Reference Check(above 5yrs) – Offer – Onboarding

(Please be aware of that the recruitment processes & schedules may be changed depending on the job and/or other circumstances. For example, onsite Interview may be replaced by video interviews due to COVID-19.)

[Others]  

• Any job post may be closed earlier at any time, if position is filled.
• In case, there is any false information shared before/during/after the entire recruitment process, we can stop our recruitment process and also withdraw our offer/hiring confirmation.
• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage. 

STRADVISION stands for an open and respectful corporate culture because we believe the diversity helps us to find new perspectives.

STRADVISION ensures that all our members have equal opportunities –regardless of age, ethnic origin and nationality, gender and gender identity, physical and mental abilities, religion and belief, sexual orientation, and social background. We always ensure diversity right from the recruitment stage and therefore make hiring decisions based on candidate’s actual competencies, qualifications, and business needs at the point of the time.

Please feel free to contact us via our talent acquisition team e-mail if you have any questions.

[STRADVISION HR Team e-mail: [email protected]]