[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]
The Role as "AI Driving Coach and Virtual World Architect"
For a team developing E2E models, the Senior Simulation Engineer is no longer a traditional QA or V&V engineer. This role is an "AI Driving Coach and Virtual World Architect." It has two key facets. The first is that of an "Adversarial Scientist," who tests the system's limits, probes its weaknesses, and scientifically investigates the root cause of failures. They actively hunt for the "unknown unknowns" that cause failures in E2E models, which lack explicit internal logic, and use their findings to enhance system robustness.
The second facet is that of an "AI Driving Coach," who creates the optimal environment for the AI to grow into the best possible driver. This goes beyond finding weaknesses; it involves designing and operating the 'digital training ground' where the AI learns and improves through millions of virtual miles, much like how Tesla advances its FSD. You must create challenging curricula, design sophisticated reward systems, and provide limitless opportunities for the AI to master safe and efficient driving skills. We are not just looking for someone to run a test suite; we are looking for a visionary thinker who will build the tools, methodologies, and vision to scientifically train, adversarially challenge, and ultimately create a safer, more capable AI driver.
The Opportunity: Building the Training Ground for AI Driving Intelligence
This role goes beyond ensuring the safety and reliability of the autonomous driving system; it plays a pivotal role in pushing our AI's performance to its limits. You will not just be testing software; you will be building the virtual world to train, validate, and ultimately enhance it. Just as Tesla trains its FSD through simulation, you will have the responsibility for designing and operating a massive digital training ground for our team's Path Planning and Control algorithms.
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]
Modular Stack (The Foundation)
- Large-Scale Learning and Evaluation Pipeline Construction: Build a large-scale simulation-based learning and optimization environment to enhance algorithm performance. Utilize vast amounts of data generated from simulations to train deep learning components within modules, such as the cost function of a path planner or the adaptive parameters of a controller, and optimize classical algorithm parameters through iterative, automated testing.
- High-Fidelity Simulation Platform Operation: Design, build, and maintain high-fidelity, scalable simulation platforms for Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), and Vehicle-in-the-Loop (VIL) testing and learning.
- Realistic Virtual Environments and Agent Modeling: Enhance physics-based sensor models by combining them with deep learning and generative AI techniques. Generate synthetic data with realistic noise and distortions to test the robustness of the perception system under various conditions. Implement surrounding agents (vehicles, pedestrians) that exhibit unpredictable, human-like interactions using reinforcement learning or data-driven behavior models to train and evaluate the modular stack's social compliance and responsiveness.
- o Performance Analysis and Automated Feedback Loops: Define Key Performance Indicators (KPIs) and metrics to quantitatively evaluate system performance, safety, and ride comfort. Build reporting dashboards and data pipelines that automatically feed analysis results back for algorithm improvement.
End-to-End Stack (The Frontier)
- Design and Build Simulation-Based Closed-Loop Learning Pipelines: Lead the development of large-scale simulation environments to train E2E driving policies from scratch and strengthen them through iterative trial and error. This is not just about replaying scenarios but creating a training ground that pushes the model's performance to its limits.
- Provide Reinforcement Learning (RL) and Imitation Learning (IL) Environments: Collaborate closely with Planning and Control engineers to define and implement effective reward functions, observation spaces, and action spaces within the simulation for learning. Develop curriculum learning strategies that allow the agent to progressively learn more difficult tasks.
- Sim2Real and Data-Driven Improvement: Research and apply strategies to minimize the Sim2Real gap, ensuring that models trained in simulation perform well in real vehicles. Build data pipelines that analyze simulation results (successes, failures, key metrics) to automatically feed back into the training dataset and continuously improve the model.
- Intelligent Edge Case Generation and Training: Use advanced techniques like importance sampling, reinforcement learning, or LLM-based generation to efficiently discover model failure modes. Generate edge cases and adversarial scenarios and integrate them into the training data to enhance the model's robustness.
Collaboration
- Collaborate with cross-functional teams, including machine learning engineers, software integration engineers, hardware platform engineers, and quality assurance, to integrate multi-vision E2E 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]
- Master's or Ph.D. in Computer Science, Mechanical, Electrical, Aerospace Engineering, Robotics, or a related field.
- 7+ years of experience in simulation, testing, and validation of complex systems like ADAS/autonomous driving or robotics.
- Expert-level proficiency in Python and strong C++ skills for building test automation frameworks and integrating with simulation environments.
- Hands-on experience with one or more industry-standard autonomous driving simulators such as CARLA, Vires VTD, NVIDIA Drive Sim, or LGSVL, and their underlying game engines (Unreal/Unity).
- Strong analytical skills and experience with large-scale data analysis and visualization.
[Preferred Qualifications]
- Experience with cloud computing platforms (AWS, Azure, GCP) and container technologies (Docker, Kubernetes) for scalable simulation.
- Knowledge of safety standards such as ISO 26262, SOTIF (ISO 21448), and UL 4600.
- Experience with HIL test benches and real-time systems.
- Background in statistics, particularly for rare-event simulation (e.g., Monte Carlo methods, importance sampling).
[Application]
- Required: Resume / Thesis (for those who have a Master’s degree or above.)
- Optional: Cover Letter, Research/Project Portfolio (including publications, open-source projects, or patents), Other theses
[Recruitment Process]
- Application Review – 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]]
[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]
The Role as "AI Driving Coach and Virtual World Architect"
For a team developing E2E models, the Senior Simulation Engineer is no longer a traditional QA or V&V engineer. This role is an "AI Driving Coach and Virtual World Architect." It has two key facets. The first is that of an "Adversarial Scientist," who tests the system's limits, probes its weaknesses, and scientifically investigates the root cause of failures. They actively hunt for the "unknown unknowns" that cause failures in E2E models, which lack explicit internal logic, and use their findings to enhance system robustness.
The second facet is that of an "AI Driving Coach," who creates the optimal environment for the AI to grow into the best possible driver. This goes beyond finding weaknesses; it involves designing and operating the 'digital training ground' where the AI learns and improves through millions of virtual miles, much like how Tesla advances its FSD. You must create challenging curricula, design sophisticated reward systems, and provide limitless opportunities for the AI to master safe and efficient driving skills. We are not just looking for someone to run a test suite; we are looking for a visionary thinker who will build the tools, methodologies, and vision to scientifically train, adversarially challenge, and ultimately create a safer, more capable AI driver.
The Opportunity: Building the Training Ground for AI Driving Intelligence
This role goes beyond ensuring the safety and reliability of the autonomous driving system; it plays a pivotal role in pushing our AI's performance to its limits. You will not just be testing software; you will be building the virtual world to train, validate, and ultimately enhance it. Just as Tesla trains its FSD through simulation, you will have the responsibility for designing and operating a massive digital training ground for our team's Path Planning and Control algorithms.
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]
Modular Stack (The Foundation)
- Large-Scale Learning and Evaluation Pipeline Construction: Build a large-scale simulation-based learning and optimization environment to enhance algorithm performance. Utilize vast amounts of data generated from simulations to train deep learning components within modules, such as the cost function of a path planner or the adaptive parameters of a controller, and optimize classical algorithm parameters through iterative, automated testing.
- High-Fidelity Simulation Platform Operation: Design, build, and maintain high-fidelity, scalable simulation platforms for Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), and Vehicle-in-the-Loop (VIL) testing and learning.
- Realistic Virtual Environments and Agent Modeling: Enhance physics-based sensor models by combining them with deep learning and generative AI techniques. Generate synthetic data with realistic noise and distortions to test the robustness of the perception system under various conditions. Implement surrounding agents (vehicles, pedestrians) that exhibit unpredictable, human-like interactions using reinforcement learning or data-driven behavior models to train and evaluate the modular stack's social compliance and responsiveness.
- o Performance Analysis and Automated Feedback Loops: Define Key Performance Indicators (KPIs) and metrics to quantitatively evaluate system performance, safety, and ride comfort. Build reporting dashboards and data pipelines that automatically feed analysis results back for algorithm improvement.
End-to-End Stack (The Frontier)
- Design and Build Simulation-Based Closed-Loop Learning Pipelines: Lead the development of large-scale simulation environments to train E2E driving policies from scratch and strengthen them through iterative trial and error. This is not just about replaying scenarios but creating a training ground that pushes the model's performance to its limits.
- Provide Reinforcement Learning (RL) and Imitation Learning (IL) Environments: Collaborate closely with Planning and Control engineers to define and implement effective reward functions, observation spaces, and action spaces within the simulation for learning. Develop curriculum learning strategies that allow the agent to progressively learn more difficult tasks.
- Sim2Real and Data-Driven Improvement: Research and apply strategies to minimize the Sim2Real gap, ensuring that models trained in simulation perform well in real vehicles. Build data pipelines that analyze simulation results (successes, failures, key metrics) to automatically feed back into the training dataset and continuously improve the model.
- Intelligent Edge Case Generation and Training: Use advanced techniques like importance sampling, reinforcement learning, or LLM-based generation to efficiently discover model failure modes. Generate edge cases and adversarial scenarios and integrate them into the training data to enhance the model's robustness.
Collaboration
- Collaborate with cross-functional teams, including machine learning engineers, software integration engineers, hardware platform engineers, and quality assurance, to integrate multi-vision E2E 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]
- Master's or Ph.D. in Computer Science, Mechanical, Electrical, Aerospace Engineering, Robotics, or a related field.
- 7+ years of experience in simulation, testing, and validation of complex systems like ADAS/autonomous driving or robotics.
- Expert-level proficiency in Python and strong C++ skills for building test automation frameworks and integrating with simulation environments.
- Hands-on experience with one or more industry-standard autonomous driving simulators such as CARLA, Vires VTD, NVIDIA Drive Sim, or LGSVL, and their underlying game engines (Unreal/Unity).
- Strong analytical skills and experience with large-scale data analysis and visualization.
[Preferred Qualifications]
- Experience with cloud computing platforms (AWS, Azure, GCP) and container technologies (Docker, Kubernetes) for scalable simulation.
- Knowledge of safety standards such as ISO 26262, SOTIF (ISO 21448), and UL 4600.
- Experience with HIL test benches and real-time systems.
- Background in statistics, particularly for rare-event simulation (e.g., Monte Carlo methods, importance sampling).
[Application]
- Required: Resume / Thesis (for those who have a Master’s degree or above.)
- Optional: Cover Letter, Research/Project Portfolio (including publications, open-source projects, or patents), Other theses
[Recruitment Process]
- Application Review – 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]]