42dot

VLA Engineer – Autonomous Driving

42dot

full-time

Posted on:

Location Type: Hybrid

Location: Pangyo • 🇰🇷 South Korea

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Job Level

Mid-LevelSenior

Tech Stack

PythonPyTorch

About the role

  • 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

Requirements

  • 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.
  • Experience with VLM / VLA-style models applied to autonomous driving or robotics (preferred)
  • Experience with closed-loop simulation, SIL/HIL, or real-vehicle testing (preferred)
  • Experience optimizing inference using TensorRT, CUDA, quantization, or pruning (preferred)
  • Experience deploying models on embedded or vehicle-grade hardware (preferred)
Benefits
  • 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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
deep learningimitation learningreinforcement learninggenerative modelingTransformer-based architecturesmultimodal learningmachine learning pipelinesdata curationmodel deploymentinference optimization
Soft skills
collaborationcommunicationproblem-solvingteamwork