Teams
At 42dot, experts across diverse roles work closely together to drive SDV innovation.
Meet our teams sharing a passion and nurturing each other’s potential.
Engineering
The Engineering organization is building the technological foundation for the SDV (Software-Defined Vehicle) era, redefining the conventional automotive paradigm.
Top engineers across AI, data, embedded systems, software,
and verification & validation collaborate to overcome hardware constraints with software and build vehicles that continuously evolve through data-driven decision-making.
With safe and reliable technologies, 42dot connects vehicles, cities, and the cloud into a unified ecosystem
— bringing to life a new mobility experience that continuously updates, adapts, and evolves on its own.
The Autonomous Driving role reinvents the concept of driving guided by industry-leading standards on safety and reliability. By developing advanced autonomous driving software that integrates perception, planning, and control, the team builds the core intelligence of the autonomous driving platform.
Driving performance is ensured through the validation process implemented across both real roads and simulation environments. Continuous data-driven learning further improves system accuracy, accelerating the realization of fully autonomous driving at scale.
- AD System: Defines autonomous driving functions and designs system architectures, while optimizing overall system performance through internationally compliant functional safety standards.
- AD Software: Develops algorithms for perception, planning, and control, strengthening system stability through ongoing algorithm refinement.
- AI Engineering: Designs and operates large-scale training and inference environments, along with cloud-native infrastructure, to power vehicle intelligence at scale.
- AI Model: Develops and advances multi-modal AI models optimized for real-world driving and service environments.
- AI Data: Collects, refines, and validates large-scale datasets spanning driving, sensor, and conversational data while managing data quality.
The AI role transforms vehicles from simple means of transportation into intelligent companions that continuously evolve alongside the user. Leveraging agentic AI that integrates voice, vision, sensors, and driving context, it delivers hyper-personalized experiences capable of understanding user intent, behavior, and even emotion.
42dot creates seamless and continuous mobility experiences that connect vehicles, mobile devices, homes, and cities through automation and context-aware recommendations tailored to user behavior patterns.
- AI Engineering: Builds ultra-low-latency real-time inference environments for seamless in-vehicle interactions, while developing and enhancing on-device and server-based agents for vehicles.
- AI Model: Researches speech models and large language models (LLMs) that understand user intent and context, and trains and continuously improves models optimized for real-world mobility services.
- AI Data: Collects, refines, and validates large-scale speech, text, and interaction data to enable natural conversations, while establishing and managing data quality as a core foundation for advancing AI models.
- MLOps: Automates the entire lifecycle of large-scale AI models—from building training pipelines to deployment in real-world vehicle and device environments, as well as real-time serving and monitoring—enabling fast, reliable, and scalable service operations.
The Data role establishes a data-driven decision-making framework by building enterprise-scale data governance and advanced analytics environments. By integrating autonomous driving, vehicle, and service data while building the engineering foundation around it, the team is accountable for the core data infrastructure behind the SDV platform.
From product planning and technology development to business strategy, the team enables informed and high-precision decision-making across the organization and also drives a trusted and scalable data-driven culture through rigorous data quality management.
- Data Engineering: Designs and builds large-scale data pipelines, while operating and optimizing the underlying data infrastructure.
- Data Analysis: Analyzes product, service, and vehicle data to derive insights and manage key performance indicators (KPIs).
- Data Governance: Establishes data standards and policies, while overseeing data security and compliance.