Senior Machine Learning/Computer Vision Engineer

Sourced from flexgen.zya.me

Parallel Systems is building autonomous battery-electric rail vehicles to transform freight transportation. This role focuses on designing and deploying deep learning perception systems, including object detection, segmentation, tracking, and scene understanding for real-world rail environments. You will own the full ML lifecycle, from data pipelines and model training to production deployment, and collaborate with autonomy, robotics, and systems teams.

• Design, develop, and deploy advanced machine learning models for large-scale perception problems.
• Own the full ML lifecycle from data mining and annotation to training, evaluation, and deployment.
• Build and optimize deep learning architectures for object detection, segmentation, tracking, pose estimation, and scene understanding.
• Develop scalable and efficient training pipelines for robust real-time inference.
• Work extensively with large image, video, lidar, and radar datasets.
• Conduct research on new architectures and integrate state-of-the-art methods.
• Build ML infrastructure and tools for data labeling, training, evaluation, and model versioning.
• Collaborate cross-functionally to integrate ML systems into real-world applications.

• Bachelor's or higher degree in Computer Science, Machine Learning, or related field.
• 4+ years of hands-on experience developing and deploying ML systems at scale.
• Strong background in computer vision and/or deep learning.
• Proficiency in Python and standard ML libraries (NumPy, SciPy, Pandas).
• Expertise in at least one deep learning framework (PyTorch or TensorFlow).
• Strong mathematical foundation in linear algebra, geometry, probability, and optimization.
• Proven ability to work autonomously and drive complex technical projects.

• Experience with multi-modal perception (sensor fusion from cameras, lidar, radar).
• Experience optimizing models for edge devices with real-time constraints.
• Background in autonomous systems, robotics, or safety-critical domains.
• Publications in top-tier ML/CV conferences (CVPR, ICCV, NeurIPS, ICML, ECCV).
• Experience with GPU/TPU programming and optimization tools (CUDA, TensorRT).