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.
Application Screening
Coding Test
First Interview (Virtual, approximately 1 hour)
Second Interview (In-person or Virtual, approximately 3 hours)
Offer Discussion / Onboarding
Please upload all required documents in PDF format.
The recruitment process may change depending on schedule and progress; the result of each stage will be sent individually to your registered email.
Please do not include legally prohibited information in your application (e.g., ID number, family relations, marital status, salary, photo, physical details, hometown).
For application errors or inquiries, contact recruit@42dot.ai.
Veterans and applicants eligible for employment protection will receive preferential consideration in accordance with applicable laws and regulations.
In compliance with the Act on Employment Promotion and Vocational Rehabilitation for Persons with Disabilities, registered individuals with disabilities will receive preferential consideration.
42dot does not accept unsolicited resumes from search firms. We will not pay any fees for resumes submitted without prior agreement.
False information in your application may result in offer cancellation.
A reference check may be conducted after the interview process, with your consent.
A 3-month probationary period may apply.
Build and operate production inference systems for large AI models, optimizing throughput, latency, GPU utilization, scalability, and reliability as customer workloads grow.
Build and operate distributed training infrastructure for large AI models, improving GPU-cluster reliability, efficiency, and scale while enabling production training pipelines and researcher workflows.
Develop and deploy production machine learning models for classification, ranking, retrieval, and personalisation, improving search relevance and model quality while balancing accuracy, latency, and inference cost.
Develop and deploy production machine learning models for classification, ranking, retrieval, and personalisation, improving search relevance and model quality while balancing accuracy, latency, cost, and reliability.
Develop and deploy production machine learning models for classification, ranking, retrieval, and personalisation, improving search relevance and model quality while balancing accuracy, latency, cost, and reliability.
