ML/DL Architect with focus on 3D computer vision

1 week ago

Lund, Sweden Schaub Technology AB Full time

Schaub Technology are looking for a Senior ML / DL Architect who is driven and solution-oriented for an end client within the Automotive Sector. Candidate must live in Sweden. Ideally you can define and develop innovative solutions for complex tasks without ready-made answers. You are prepared to leave no stone unturned to find the right way forward and thrive in an environment where standard solutions are not sufficient. Responsibilities: Define solutions and develop models for complex perception applications in autonomous systems. Build and optimize training frameworks and data pipelines. Create production-ready embedded software for self-driving vehicles. Collaborate with stakeholders to define requirements and key performance indicators (KPIs). Maintain and improve already implemented algorithms in vehicles and compute clusters. Leverage expertise in Fusion techniques, which involve merging multiple data sources, models, or representations to achieve superior results. This includes combining information from different sensors such as cameras, LiDAR, and audio, as well as mixing multiple datasets to improve model generalization and robustness. Requirements: Experience in architecture and design of ML/DL models, especially for non-standard tasks (i.e., not image classification, object recognition, or segmentation). Strong background in 3D computer vision and experience working with point cloud-based models. Experience developing, training, and deploying deep learning models for perception in embedded systems, preferably within the automotive industry. Good programming skills in Python and C/C++, as well as experience with TensorFlow and/or PyTorch. Experience in embedded software development and understanding of sensor integration. Knowledge of data management, data preparation, annotation, and quality assurance. Proven expertise in Fusion methodologies, including sensor fusion (merging data from cameras, LiDAR, radar, audio, etc.) and dataset fusion to enhance model performance and generalization. Advantageous to have experience with CNNs, Transformers, RNNs, MLOps, orchestration, simulation, Docker/Kubernetes, and project management.

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