We are an Engineering and IT recruitment agency tasked with finding a well rounded Algorithm Engineer for a client of ours. Our client is an innovative leader in the autonomous driving sector, developing cutting-edge systems that transform traditional vehicles into intelligent, self-navigating machines. Their modular, vehicle-agnostic platform leverages advanced perception, decision-making, and motion-planning algorithms to enable safe, scalable deployment of self-driving technology across a wide range of environments.
With a culture grounded in deep technical expertise, iterative R&D, and collaborative problem-solving, this team pushes the limits of real-time AI, robotics, and embedded software to bring full autonomy from concept to road.
Typical Duties and Responsibilities
As a Senior Algorithm Engineer, you’ll play a critical role in designing, implementing, and optimizing perception and planning algorithms that directly power autonomous vehicle decision-making. Working alongside cross-functional teams, you will help bridge the gap between theoretical models and real-world implementation, ensuring reliable performance in dynamic driving conditions.
Key Responsibilities Include:
- Develop, implement, and optimize algorithms for perception, localization, sensor fusion, and motion planning within ROS/ROS2 and Apollo frameworks
- Use HD map tools to integrate real-time environmental inputs with high-resolution mapping data for accurate path prediction and obstacle avoidance
- Leverage CARLA simulation to design, test, and validate autonomous driving algorithms in complex and diverse traffic scenarios
- Apply deep learning using TensorFlow or PyTorch to improve object detection, segmentation, and behavior prediction from LiDAR, radar, and camera inputs
- Work closely with FPGA and ASIC development teams to optimize algorithms for deployment on NVIDIA DRIVE AGX and other embedded compute platforms
- Conduct data analysis using MATLAB/Simulink to support modeling, control system development, and verification workflows
- Collaborate with systems and software engineers to improve labeling strategies, data pipelines, and validation metrics using advanced labeling platforms
- Document algorithm performance, conduct code reviews, and contribute to the integration of robust safety and redundancy measures in system architecture
Education
- Bachelor’s degree in Electrical Engineering, Computer Engineering, Robotics, or a related technical field is required
- Master’s or Ph.D. in a relevant field (e.g., Machine Learning, Control Systems, Autonomous Systems) is strongly preferred
Required Skills and Experience
- 7+ years of professional experience in algorithm development for autonomous systems, robotics, or embedded AI
- Strong proficiency in C++ and Python, with production-level experience integrating algorithms into real-time systems
- Hands-on expertise with ROS and ROS2, including creating custom nodes, launching complex graphs, and interfacing with hardware abstraction layers
- Deep understanding and application of Apollo by Baidu, especially in behavior planning, control, and localization modules
- Proven track record of using CARLA or other simulation tools for autonomous systems development and validation
- Proficient in developing deep learning models with TensorFlow or PyTorch for vision and perception tasks
- Experience working with HD mapping, route planning, and traffic rule integration
- Familiarity with LiDAR, radar, and camera data processing, as well as calibration and synchronization techniques
- Experience optimizing algorithms for embedded deployment, ideally using NVIDIA DRIVE AGX, FPGAs, or custom ASICs
- Strong modeling, analysis, and visualization skills using MATLAB/Simulink
- Experience with labeling and data annotation platforms (e.g., Scale AI, Supervisely, CVAT)
Preferred Qualifications
- Experience developing for real-time, safety-critical automotive or aerospace systems (e.g., ISO 26262, MISRA C++)
- Prior experience with sensor fusion across heterogeneous input streams (LiDAR, radar, IMU, GPS)
- Contributions to open-source robotics or AV projects (e.g., Autoware, Apollo, ROS ecosystem)
- Strong understanding of control theory, SLAM, Kalman filters, or reinforcement learning as applied to motion planning
- Knowledge of CAN bus systems, vehicle dynamics modeling, and system-level architecture in automotive platforms
- Prior work in closed-loop simulations and hardware-in-the-loop (HIL) test environments
- Experience leading or mentoring junior engineers in a collaborative, Agile development environment
