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We are looking for the best About Us 42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of…
Staff embedded firmware engineer acting as a technical anchor for 42dot's real-time software stack behind Hyundai's software-defined vehicle platform. Day to day: architecting BSPs, peripheral drivers, and RTOS environments in embedded C/C++ under MISRA and ISO 26262, leading silicon bring-up in Sunnyvale labs, and driving root-cause debugging.
Senior embedded firmware engineer building the real-time C/C++ software stack for 42dot's software-defined vehicle platforms (Hyundai's Global Software Center) — writing MISRA/ISO 26262-compliant drivers, BSPs, and RTOS logic, bringing up prototype hardware, and debugging low-level issues in the Sunnyvale lab.
Build and operate the AWS and Kubernetes cloud infrastructure that powers 42dot's autonomous-driving data and ML workloads. Day to day you'll own CI/CD pipelines, multi-region data environments, on-call and incident analysis, and developer tooling using Terraform/Pulumi, Python/Go, and GitOps practices.
About the Team & Mission 42dot의 자율주행 VLA 팀은 Vision-Language-Action (VLA) 모델을 통해 차량이 복잡한 도로 상황을 이해하고, 판단을 주행 행동으로 연결하도록 만드는 기술을 개발합니다. 실제 주행 데이터, 모델 학습, closed-loop simulation과 실차 검증을 연결해 모델의 취약점을 발견하고 주행 성능을 지속적으로…
A 6-month contract PMO role at 42dot supporting autonomous driving development in Pangyo: coordinating meetings and action-item tracking in Jira/Confluence, operating and managing self-driving test vehicles and their SW/HW records, handling development parts and procurement, and supporting the Pleos 27 program.
Owns the end-to-end security architecture of 42dot's software-defined-vehicle real-time platform: secure boot chain, Uptane-based OTA update trust model, key management, and ISO/SAE 21434 / UNECE R155/R156 compliance. Day to day is embedded C on MCU-class HSMs, threat modeling, and reviewing implementations.
Deep learning engineer on 42dot's Gleo AI team building production text-to-speech (TTS) for in-vehicle voice experiences. Day-to-day work spans optimizing TTS models for on-device/NPU environments (quantization, memory/latency), streaming synthesis, and multilingual/multi-speaker models using LLMs and flow matching, with PyTorch, TensorRT/ONNX, Python/C++ on Linux.
Senior Product Designer on 42dot's Pleos Playground team, designing the end-to-end experience of the in-vehicle (IVI) App Market, its partner/operator management service, and the Playground website within a software-defined vehicle ecosystem. Daily work centers on UX/UI and interaction design, prototyping and usability validation, localization structure, and collaboration with PM, engineering, and
Product Manager for 42dot's Pleos Playground Developer Platform, owning the end-to-end lifecycle of the in-vehicle App Market — strategy, requirements, development, mass production, and operations — for third-party apps in Software Defined Vehicles. Requires 8+ years in product/service/UX planning and cross-team collaboration.
Designs and optimizes reinforcement learning pipelines to train production-ready large language models, focusing on alignment algorithms and reward stability.
About the Team & Mission Gleo AI는 현대차 더 뉴 그랜저에 처음 적용된 LLM 기반 차량용 음성 AI 에이전트입니다. 사용자는 정해진 명령어를 외우지 않아도 차량과 자연스럽게 대화하며 내비게이션, 전화, 웹 검색 기능 등을 제어할 수 있습니다. Gleo는 발화자의 위치, 대화 맥락, 주행 상황을 함께 이해하고, 사용자의 의도를 처리하기 위해 적합한…
Builds Android apps that let users remotely monitor and control vehicles via BLE/UWB, digital keys, OTA, and charging features using Kotlin, Jetpack, and Clean Architecture.
QA Engineer at 42dot's Vietnam QA hub validating HMI quality for in-vehicle infotainment (IVI) systems: designing and executing test cases, comparing implemented UIs against design specs, documenting reproducible defects, and automating UI validation with tools like adb, Appium, and OCR-based checks.
Senior hands-on engineer supporting 42dot's autonomous driving and software-defined vehicle programs in Sunnyvale: integrating and validating sensors (camera, radar, lidar, GNSS/IMU), compute platforms, and vehicle networks (CAN, Ethernet), fabricating prototype hardware, and keeping the R&D test fleet safe, calibrated, and mission-ready.
Design, generate, evaluate, and improve LLM training data (instruction, preference, reasoning, domain-specific) and build automated synthetic data generation pipelines using Python and LLMs.
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