Autonomous Driving Technical Lead

Project description

We are seeking an experienced Autonomous Driving Technical Lead – Level 4 Systems to contribute to the development, integration, maintenance, and continuous advancement of an existing Level 4 autonomous driving system for robotic vehicle platforms. The role combines deep expertise in autonomous driving and physical AI with hands-on software engineering and technical leadership. The successful candidate will take ownership of complex technical topics, analyze unresolved system and software problems, identify root causes, develop feasible solution approaches, and contribute directly to their implementation and validation. Working across technical and organizational boundaries, the Technical Lead will facilitate technical alignment, knowledge transfer, and consistent engineering approaches. The position requires the ability to work both at system level and directly with production code, algorithms, architecture, sensor data, simulation results, and vehicle measurements.

Responsibilities

• The Technical Lead will analyze complex and cross-functional issues within the Level 4 autonomous driving stack. This includes reviewing source code, architecture, logs, recorded sensor data, simulation results, test evidence, and vehicle behavior to distinguish between algorithmic, software, integration, calibration, timing, performance, hardware, interface, and data-quality issues. Based on these analyses, the Technical Lead will develop and evaluate solution options considering technical feasibility, risks, dependencies, implementation effort, and overall system impact. Recommendations must be evidence-based and clearly documented. Where appropriate, prototypes or proofs of concept will be created to validate proposed approaches before contributing to their implementation, integration, and verification. The role will provide technical direction by translating high-level objectives and identified issues into actionable engineering activities. This includes reviewing architecture decisions, algorithm choices, interfaces, implementation approaches, and development results with respect to robustness, functional coverage, performance, maintainability, and suitability for production use. Technical risks, dependencies, inconsistencies, knowledge gaps, and engineering blockers must be identified early and driven toward sustainable resolution. Design reviews, code reviews, architecture discussions, and structured troubleshooting activities are therefore integral parts of the position. Autonomous Driving and Physical AI Expertise The role requires a strong understanding of the interaction between perception, localization, sensor fusion, prediction, behavior planning, motion planning, vehicle interfaces, and the underlying hardware and software platform. Relevant technical areas include: Point-cloud processing, occupancy grids, ground-plane estimation, and voxel-based representations Object detection, classification, segmentation, tracking, and state estimation Multi-modal sensor fusion across cameras, LiDAR, radar, GPS, and IMUs Localization, SLAM, local-map generation, and HD mapping Scene understanding, behavior modelling, maneuver classification, and trajectory prediction Behavior planning, trajectory generation and optimization, collision checking, and decision-making under uncertainty Machine-learning and deep-learning methods for autonomous systems, including transformer-based perception and occupancy networks Relevant physical-AI approaches such as end-to-end and imitation learning, world models, vision-language-action models, and foundation models for driving and robotics The Technical Lead will assess current research and industry developments and determine where emerging technologies can provide practical value to the production system. New approaches must be evaluated critically with regard to maturity, explainability, safety, integration effort, computational requirements, and operational benefit. Software Development, Integration, and Validation The position requires direct involvement throughout the software development lifecycle. The Technical Lead will work with existing architectures and codebases, implement or review production-grade software, and ensure that proposed solutions can be integrated into the overall autonomous driving system. The role will contribute to meaningful test scenarios, technical acceptance criteria, and validation approaches. Defects identified during software, simulation, system, hardware, or vehicle integration must be analyzed systematically and traced to their technical causes. Relevant environments and activities may include: SIL, MIL, PIL, HIL, vehicle-in-the-loop, and on-vehicle testing CARLA, LGSVL, or comparable simulation environments ROS bag, PCAP, and sensor-data replay Automated regression testing and CI/CD pipelines Closed-loop scenario evaluation Calibration and time-synchronization analysis Fault-injection, stress, and robustness testing CPU and GPU performance analysis The Technical Lead will contribute to integration and release-readiness assessments by evaluating functional behavior, interfaces, performance, edge-case coverage, and remaining technical risks. Suitable KPIs will be defined or reviewed for perception accuracy, prediction and planning quality, latency, resource consumption, robustness, and overall system stability. *Technical Collaboration *As a central technical interface across the participating engineering organizations, the Technical Lead will establish a detailed understanding of the existing stack, architecture, development environment, operational constraints, and current technical challenges. The role facilitates the exchange of technical knowledge and aligns objectives, interfaces, assumptions, dependencies, and solution approaches with the relevant specialists. Technical workshops, design and architecture reviews, code reviews, and troubleshooting sessions will be used to establish a shared technical understanding and translate decisions into actionable engineering guidance. Periodic on-site collaboration and vehicle testing in Fribourg, Switzerland, form part of the role.

SKILLS

Must have

• Required Qualifications and Experience Candidates should have an MSc or PhD in robotics, computer science, software engineering, artificial intelligence, automotive engineering, electrical engineering, or a related technical discipline. Equivalent industry experience may be considered. A minimum of three years of hands-on software development experience in autonomous driving, ADAS, mobile robotics, or a directly comparable environment is required. The candidate should have contributed to the development or maintenance of production-grade autonomous systems. Experience with a live, deployed, or operational Level 4 system is highly desirable. The successful candidate should also bring: Deep expertise in perception, sensor fusion, localization, prediction, behavior planning, or motion planning Strong programming skills in C++ and Python The ability to read, review, and reason about complex production code Practical experience with ROS or ROS 2 and Linux-based development environments Experience with real-time or performance-critical software systems A strong background in machine learning or deep learning applied to autonomous systems The ability to understand unfamiliar architectures and codebases quickly Experience analyzing system behavior using logs, sensor recordings, simulations, measurements, and test results Experience reviewing technical solutions and facilitating evidence-based engineering decisions Very good written and spoken English Willingness to travel periodically to Fribourg, Switzerland Personal Competencies The ideal candidate is analytical, structured, self-driven, and solution-oriented. They take ownership of complex or insufficiently defined technical topics and pursue them proactively toward technically sound and sustainable solutions. The role requires the ability to challenge assumptions constructively, communicate complex findings clearly, and formulate recommendations based on objective evidence. The successful candidate combines high technical standards with a pragmatic approach and is willing to contribute directly to software development, technical analysis, integration, troubleshooting, and validation.

Nice to have

Preferred Qualifications Experience in the following areas would be beneficial: Previous responsibility as a technical lead, staff engineer, principal engineer, or comparable senior technical role Maintenance, operation, or scaling of a deployed Level 4 autonomous vehicle system SLAM, HD mapping, and localization Transformer-based perception, occupancy networks, end-to-end learning, or imitation learning Embodied AI, world models, and foundation models for driving or robotics Automotive communication, diagnostics, sensor interfaces, calibration, and time synchronization Embedded compute platforms, SoCs, CPUs, and GPUs Functional Safety, ISO 26262, ISO 21448/SOTIF, and AV safety-case maintenance Publications or relevant open-source contributions in autonomous driving, robotics, or physical AI