AI Engineer

• 3D Gaussian Splatting(3DGS) 및 생성형 AI 기술을 활용한 고정밀 3D 환경 재구성
• AI 모델의 Closed-loop Testing을 위한 시뮬레이션 파이프라인 설계 및 개발
• 차량 동역학 및 운동학 기반의 물리 모델 구현 및 검증
• C++, Python, CUDA 기반의 고성능 시뮬레이션 모듈 개발 및 최적화
• Perception, Planning, Control 알고리즘의 시뮬레이션 환경 통합
• 시뮬레이션 정확도 향상 및 Sim-to-Real Gap 최소화를 위한 연구 및 개발
• Utilize 3D Gaussian Splatting (3DGS) and generative AI technologies to reconstruct high-fidelity 3D environments
• Design and develop scalable simulation pipelines for closed-loop testing of AI models
• Implement and validate physics-based vehicle dynamics and kinematics models
• Develop and optimize high-performance simulation modules using C++, Python, and CUDA
• Integrate perception, planning, and control algorithms into simulation environments
• Research and develop methods to improve simulation fidelity and reduce the Sim-to-Real gap

Senior Physical AI Engineer – Simulation

Computer Vision Engineer

• 자율주행 기술구현을 위한 3차원 컴퓨터비젼 및 기계학습 알고리즘 연구 개발
• 3D shape modeling and processing
• Object pose estimation and tracking
• 효율적이고 확장 가능한 비전 솔루션 개발
• Vision과 Robotics 접목
• 저수준 및 물리 기반 비전 알고리즘 연구
• 대규모 비지도 학습로부터의 자기 감독 표현 학습 수행
• 자율주행을 위한 월드 모델 및 클로즈드 루프 시뮬레이션

Senior Computer Vision Engineer – Autonomous Driving

• 자율주행 데이터를 활용한 Vision-Language-Action 기반 driving foundation model 설계 및 개발
• Camera/video, map, trajectory, action, language 등 multimodal sequential data 기반 모델 학습 및 평가
• Driving scene understanding, temporal reasoning, agent interaction modeling, risk/event understanding 모델 개발
• Scene captioning, visual question answering, auto-labeling, data mining, retrieval 등 VLM 기반 응용 기능 개발
• 대규모 multimodal dataset 구축, 학습 recipe, evaluation benchmark, ablation 실험 설계
• Simulation, Data, ML Platform 조직과 협업하여 모델을 자율주행 시스템에 통합

Senior AI Engineer – Driving VLM/VLA

• Driving VLA/World Model/Planning 모델의 closed-loop evaluation framework 설계 및 개발
• Log replay, scenario-based simulation, regression test, safety-critical case mining pipeline 구축
• RL, offline RL, reward modeling, policy distillation, post-training 기반 driving policy 개선
• Simulation rollout, reward function, evaluation metric, failure analysis pipeline 설계 및 운영
• Safety, comfort, progress, rule compliance 등 자율주행 품질 지표 정의 및 모델 평가 체계 구축
• Counterfactual simulation, synthetic scenario, real-world driving log를 활용한 model improvement loop 개발
• VLM/VLA, Data, ML Platform 조직과 협업하여 closed-loop 학습/평가 시스템 통합

Senior AI Engineer – Closed-loop Simulation, RL

• Data preprocessing, model training, and performance validation for end-to-end trajectory generation and decision-making models
• Model-Based Reinforcement Learning (MBRL) and Imitation Learning (IL) algorithm development and optimization
• Integration and validation of motion planning, filtering (e.g., Kalman Filter, Particle Filter), and navigation algorithms
• Support on-device optimization of deep learning models and planning pipelines using CUDA for real-time performance
• Validate AI models and algorithms in simulation and real vehicle environments
• Collaborate with perception, planning, and control teams to develop next-generation physical AI systems

Physical AI Engineer – Model

• 자율주행 시스템 핵심 기술에 대한 설계, 구현, 검증까지 End-to-End 수행
• 실차 데이터 기반 알고리즘/모델 설계 및 검증
• 시뮬레이션 및 실도로 실험을 통한 성능 분석 및 개선
• 연구 결과를 실제 시스템에 적용 가능한 형태로 구현
• 실제 프로덕션 팀과의 긴밀한 협업을 통한 문제 해결

AI Engineer – MS/PhD Intern

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Developer – AI Neobank App

LLM Engineer

• 대규모 언어 모델(LLM)의 추론 성능(Latency, Throughput, Memory Efficiency)을 최적화합니다.
• 다양한 모델 구조 및 추론 환경에 맞는 최적화 기법을 연구하고 적용합니다.
• Long Context, Multi-turn Conversation 등 실제 서비스 환경에서의 성능을 개선합니다.
• GPU 및 Accelerator 기반 LLM Inference Engine을 개발하고 최적화합니다.
• vLLM, TensorRT-LLM, SGLang, llama.cpp, ONNX Runtime, MLX 등 최신 Inference Framework를 활용하거나 개선합니다.
• Quantization(MXFP8, NVFP4, AWQ, GPTQ 등) Pruning, Distillation 등 모델 경량화 기법을 연구하고 적용합니다.
• Mobile, Embedded, Edge Device 환경에서 LLM을 효율적으로 실행하기 위한 최적화 기술을 개발합니다.
• 다양한 하드웨어 및 Inference Backend의 성능을 분석하고 Benchmark를 수행합니다.

LLM Engineer – Optimization

Applied AI Engineer – Finance Super App

• Build AI-powered product features integrated into real insurance workflows.
• Design and optimize LLM-based interactions across customer and internal systems.
• Develop prompts, reasoning flows, and structured AI outputs for production use cases.
• Evaluate model performance using real-world data, feedback loops, and experiments.
• Improve reliability of AI outputs through guardrails, fallback logic, and validation layers.
• Work closely with backend engineers to integrate AI capabilities into APIs and workflows.
• Design and implement AI-driven automation features (chat, recommendations, decision support).
• Analyze user behavior and model responses to continuously improve AI performance.
• Collaborate with product, design, and engineering teams to ship end-to-end AI features.
• Contribute to AI system design decisions focused on usability and real-world impact.

Machine Learning Engineer jobs in South Korea

See all jobs on JobTailor