Sr. Simulation/Evaluation Learning Algorithm Engineer (Multi-Camera Vision Simulation/Evaluation)
직무 설명
This position is with STRADVISION Seoul Office
[About STRADVISION]
We Empower Everything To Perceive Intelligently
STRADVISION is a global deep-tech company specializing in autonomous driving and vision AI, making life safer and better through AI-based camera perception technology. Since its founding in 2014, the company has developed and commercialized core AI technologies that accurately perceive and interpret the world around us. In 2026, STRADVISION became the first autonomous driving software company to be listed on KOSDAQ. Today, approximately 250 colleagues work together across eight offices worldwide.
STRADVISION has secured contracts to supply its camera perception software, SVNet, to 14 global automakers for more than 50 vehicle models. SVNet has been deployed in over five million production vehicles. STRADVISION won the Gold Award for “Best-in-Class Software for Perception Systems” at the AutoSens Awards for two consecutive years, in 2021 and 2022.
The company is now expanding the reach of vision AI beyond autonomous driving into robotics and physical AI. Join us in shaping the future of mobility and physical AI.
[Our Story]
🔎 STRADVISION’s Core Technology
🔎 STRADVISION's Perks & Benefits
Latest News from STRADVISION
📌 STRADVISION Unveils Its SVDataFlow Pipeline, Focusing on Technical Advancement and Scalability
[Our Technology]
- In 2019, STRADVISION became the world’s first deep learning-based startup to achieve ASPICE CL2 certification. In 2020, the company received the AVT ACES Autonomous Vehicle Innovation Award.
- STRADVISION won the Gold Award for “Best-in-Class Software for Perception Systems” at the AutoSens Awards for two consecutive years, in 2021 and 2022.
- In August 2022, STRADVISION closed a KRW 107.6 billion Series C funding round, with Aptiv of the United States and ZF Group of Germany participating as strategic investors, reflecting global recognition of the company’s technology.
- STRADVISION holds 168 U.S. patents related to AI and automotive technology and continues to invest actively in research and development to further strengthen its technological capabilities.
- In the first half of 2026, the cumulative number of vehicles equipped with SVNet exceeded five million worldwide. STRADVISION continues to grow despite an economic slowdown and intensifying industry competition.
- In 2025, STRADVISION ranked 10th in Korea’s AI competitiveness rankings, behind companies such as Samsung Electronics, NAVER, and LG.
[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.
- 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.
- 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.
- 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
- Application Review > Screening Test > Interview(s) > Reference Check > Offer > Onboarding
- Please be aware of that the recruitment processes & schedules may be changed depending on the job and/or other circumstances.
[Others]
- This position is open for ongoing recruitment and may close earlier once the role has been successfully filled.
- A 3-month probationary period will apply after joining as a regular employee.
- Visa sponsorship can be supported for eligible international candidates.
- If any false or inaccurate information is identified during the hiring process, the recruitment may be discontinued or an offer may be withdrawn.
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 HR team e-mail if you have any questions.
[STRADVISION HR Team e-mail: [email protected]]
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