스트라드비젼(STRADVISION) - Sr. Simulation Learning Algorithm Enginneer

회사 소개

This position ​is ​with ​STRADVISION Seoul ​Office

[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 : https://buly.kr/Edu2Jdr
• STRADVISION’s Core Technology : https://buly.kr/74XS8bi
• STRADVISION's Welfare : https://buly.kr/BpFucdd

[Our Technology]

STRADVISION's Technology : https://stradvision.com/_ENG/dh/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.

주요 업무

[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.
• 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.

자격요건

[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.

우대사항

[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).

기술 스택

Python C C++ Jenkins Docker OpenCV GCC

복지 및 혜택

• 선택적근로시간제로 유연한 출퇴근 가능 (필수근무시간 : 11:00 ~ 17:00)
• 주 1회 재택 근무
• 최신 IT 장비 및 허먼밀러 의자 제공
• 안마의자와 리클라이너가 구비되어있는 편안한 휴게실
• 무제한 도서 구매 지원
• 특허 등록 및 논문 출품 지원
• 단체상해보험, 경조사 및 직계가족 상조서비스 지원
• 종합건강검진 및 검진당일 1일 휴가 지원
• 난임시술비 지원 (연 200만원 이내 실비 지원)
• 3, 6, 9년마다 재충전을 위한 근속휴가 제공
• 여유로운 귀향/귀가를 위한 명절 전 반일 휴무
• 연말에 찾아오는 SV 겨울잠 휴가
• 불가피한 야근시 식대(15,000원) 및 택시비 지원

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