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42dot
42dot
51 – 200 Employees
AutomotiveConsultingLogistics
42dot is a mobility AI company working at the intersection of automotive technology and logistics. Its work focuses on software-defined vehicle technologies, autonomous electric vehicles, and the software and data infrastructure needed for scalable urban transportation. The company develops vehicle software, fleet data platforms, and AI models for perception and control, alongside TAP!, its mobility operating system for deploying autonomous services. Through research and collaboration on vehicle electronics and software-first architectures, 42dot aims to make mobility systems more autonomous, adaptable, and easier to operate.
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Reinforcement Learning Engineer, Autonomous Driving

Develop and optimize reinforcement learning policies for 42dot’s autonomous-driving models. Build self-play simulations, scenario rollouts, and safety evaluations to improve driving performance.

KRSouth KoreaHybridFull-timeMiddleSeniorEnglish RequiredKorean RequiredEngineer
PythonPyTorch

Description

  • Design observation and action interfaces between VLA policies and simulators, and develop closed-loop reinforcement learning post-training using methods such as GRPO and PPO.
  • Build self-play training in vector- or occupancy-based driving simulators, where the ego vehicle and traffic agents interact with current policies and previous checkpoints.
  • Develop candidate action and trajectory rollouts across possible future scenarios, then select actions using expected return and safety constraints.
  • Evaluate policy safety and generalization with closed-loop benchmarks.
  • Analyze reward hacking and simulator bias.
  • Lead promising reinforcement learning ideas from design through large-scale training and rigorous evaluation to integration with driving models.

Requirements

  • At least three years of experience in machine learning, reinforcement learning, or robotics, or equivalent research and development capability.
  • Experience implementing policy learning algorithms and conducting training and evaluation with Python and PyTorch.
  • Understanding of policy optimization, reward design, and credit assignment, with the ability to design reinforcement learning experiments.
  • Experience training policies in simulation or multi-agent environments and verifying improvements against baselines.
  • English communication skills sufficient to discuss technical issues and collaborate with overseas offices.
  • Experience developing self-play, multi-agent reinforcement learning, or policy population training systems (preferred).
  • Experience applying reinforcement learning post-training methods such as GRPO or PPO to VLA or generative policies (preferred).
  • Experience training policies in vector- or occupancy-based driving simulators, or developing world-model-based rollout planning (preferred).
  • Experience with large-scale parallel rollouts, distributed training, or sim-to-real validation (preferred).

Benefits

  • Preference is given to veterans and people eligible for employment protection under applicable laws.
  • Preference is given to applicants with a registered disability certificate.
  • Reference checks may be conducted with the applicant’s consent after the interview process.
  • A three-month probationary period may apply.

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