We are looking for the best

About Us

At 42dot, we build multimodal driving models that connect visual perception, scene context, and driving actions to enable robust autonomous driving behavior.
Our goal is to advance planning, decision-making, and action generation in real-world driving environments by leveraging VLA-style modeling approaches that incorporate prior knowledge for improved generalization, with a strong focus on production deployment and on-vehicle execution.

Responsibilities

• Design and develop VLA-based models for autonomous driving, focusing on planning, decision-making, and action generation

• Apply imitation learning, reinforcement learning, and generative modeling to improve driving behavior and long-horizon decision-making

• Fine-tune and adapt multimodal models (VLM / VLA-style architectures) for autonomous driving tasks using large-scale driving datasets

• Define and evaluate multimodal representations for driving data (video, BEV, map, vehicle state, actions, optional language annotations)

• Build and maintain end-to-end machine learning pipelines from data curation and training to evaluation and deployment

• Evaluate models in open-loop, closed-loop simulation, and real-vehicle environments, with a focus on safety and robustness

• Collaborate with perception, prediction, planning, control, and platform teams to integrate ML models into production vehicle software stacks

Qualifications

• Strong hands-on experience with deep learning models for autonomous driving, robotics, or sequential decision-making systems

• Practical experience applying imitation learning and/or reinforcement learning to real-world problems

• Solid understanding of Transformer-based architectures and multimodal learning

• Proven experience deploying machine learning models in production or safety-critical systems

• Strong programming skills in Python and experience with PyTorch

• Experience working with large-scale datasets and distributed training environments

• Ability to collaborate effectively across software, vehicle, and hardware teams

Preferred Qualifications

• Experience with VLM / VLA-style models applied to autonomous driving or robotics

• Experience with closed-loop simulation, SIL/HIL, or real-vehicle testing

• Experience optimizing inference using TensorRT, CUDA, quantization, or pruning

• Experience deploying models on embedded or vehicle-grade hardware

• Research or engineering contributions in autonomous driving, robotics, or machine learning

Interview Process

• 서류전형 - 코딩테스트 - 화상면접 (1시간 내외) - 대면 혹은 화상면접 (3시간 내외) - 최종합격

• 전형절차는 직무별로 다르게 운영될 수 있으며, 일정 및 상황에 따라 변동될 수 있습니다.

• 전형일정 및 결과는 지원서에 등록하신 이메일로 개별 안내드립니다.

Additional Information

• 이력서 제출 시 주민등록번호, 가족관계, 혼인 여부, 연봉, 사진, 신체조건, 출신 지역 등 채용절차법상 요구 금지된 정보는 제외 부탁드립니다.

• 모든 제출 파일은 30MB 이하의 PDF 양식으로 업로드를 부탁드립니다. (이력서 업로드 중 문제가 발생한다면 지원하시고자 하는 포지션의 URL과 함께 이력서를 recruit@42dot.ai으로 전송 부탁드립니다.)

• 인터뷰 프로세스 종료 후 지원자의 동의하에 평판조회가 진행될 수 있습니다.

• 국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다.

• 장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.

• 42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다.

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