Algorithm Architect - ADAS

What matters most

• End-to-End Algorithmic Architecture

  The role requires defining and owning the full stack for ADAS functions like ACC, LKA, and AEB. You must demonstrate the ability to decompose complex system-level requirements into modular components covering perception, fusion, prediction, planning, and control while ensuring they integrate seamlessly into embedded and high-performance computing environments.

• Embedded Optimization and Real-Time Constraints

  You must balance high-accuracy algorithmic performance with strict embedded constraints including latency, memory efficiency, and real-time execution on ECUs and GPUs. This requires deep knowledge of hardware-software interfaces and the ability to optimize AI/ML models for deployment on automotive-grade hardware.

• Functional Safety and SOTIF Compliance

  As the technical authority, you are responsible for ensuring all algorithmic designs adhere to ISO 26262 (Functional Safety) and ISO 21448 (SOTIF). You must be prepared to integrate safety-critical design principles into the development lifecycle from the architectural phase through to validation.

Likely challenges to think through

• Bridging Traditional Control and AI/ML

  The role demands a hybrid approach, combining traditional signal processing and control theory with modern AI/ML perception pipelines (CNNs, Transformers). Prepare to discuss how you manage the trade-offs between the deterministic nature of classical control and the probabilistic nature of deep learning in safety-critical automotive systems.

• Validation Complexity in Simulation and HIL

  Validating complex ADAS algorithms requires mastery of SIL/HIL and on-vehicle testing. The challenge lies in creating robust simulation environments that accurately represent real-world edge cases. Prepare to explain your methodology for ensuring simulation results correlate with physical vehicle performance and how you handle data synchronization across multiple sensors.

Topics to prepare

• Functional Safety and SOTIF Compliance

  Review ISO 26262 and ISO 21448 standards specifically regarding algorithmic safety requirements. Exercise: Map a hypothetical AEB (Automatic Emergency Braking) function to its corresponding ASIL level and identify potential failure modes that require mitigation strategies to satisfy SOTIF.

• Sensor Fusion and State Estimation

  Deepen understanding of combining heterogeneous sensor data (Camera, Radar, IMU, GNSS) for environment modeling. Exercise: Compare the mathematical trade-offs between Extended Kalman Filters (EKF) and Particle Filters for tracking a moving object in a high-latency environment.

• Embedded System Architecture for ADAS

  Understand the constraints of deploying complex AI models on automotive ECUs and SoCs. Exercise: Design a high-level architecture diagram showing the data flow from raw sensor input to control output, highlighting where hardware acceleration (GPU/NPU) is utilized versus CPU-based processing.

Skills to revise

• Algorithm Optimization for Embedded Systems

  Focus on balancing model accuracy with real-time latency and memory constraints. Exercise: Refactor a Python-based perception prototype into C++ and apply quantization or pruning techniques to demonstrate a reduction in inference latency for a CNN-based object detection model.

• MATLAB/Simulink for Control Design

  Utilize Simulink for modeling vehicle dynamics and control loops. Exercise: Build a simple longitudinal control model for Adaptive Cruise Control (ACC) in Simulink and perform a SIL (Software-in-the-Loop) simulation to verify performance against a step-input target speed.

• ROS/ROS2 Framework Integration

  Gain proficiency in using ROS/ROS2 for modular ADAS development. Exercise: Create a custom ROS2 node that subscribes to a sensor data topic, performs a basic transformation or filtering operation, and publishes the processed data to a planning module.

• Prepare a technical case study on sensor fusion architecture

  Develop a structured walkthrough of a past project where you designed a multi-sensor fusion framework (e.g., Camera, Radar, IMU). Focus on how you handled time synchronization, calibration, and state estimation techniques like Kalman Filters to achieve robust object tracking.

• Document your experience with ISO 26262 and SOTIF

  Prepare specific examples of how you have integrated functional safety and SOTIF requirements into the algorithmic design phase. Be ready to explain how you balanced performance optimization with safety-critical constraints in an embedded environment.

• Review embedded deployment and hardware acceleration strategies

  Be prepared to discuss your experience optimizing algorithms for specific SoC or GPU architectures (e.g., NVIDIA, Qualcomm). Explain your approach to managing latency, memory efficiency, and real-time execution within AUTOSAR or RTOS environments.

• Formulate questions regarding L&T's ADAS roadmap

  Prepare thoughtful questions about the company's current focus on perception pipelines, such as their transition toward transformer-based architectures or their specific challenges in scaling ADAS functions like ACC and LKA for different vehicle platforms.

• Refine your leadership and mentoring narrative

  Prepare concrete examples of how you have mentored junior algorithm engineers, managed technical design reviews, and fostered collaboration between cross-functional teams like data science and software engineering to deliver production-ready ADAS features.