Senior Machine Learning/Computer Vision Engineer

Senior Machine Learning/Computer Vision Engineer

Parallel Systems is seeking an experienced Machine Learning Engineer to help build the next generation of perception systems powering our fully autonomous, battery-electric rail vehicles. In this role, you’ll take ownership of designing and deploying cutting-edge deep learning models that enable our vehicles to perceive and reason about complex, real-world environments. From handling adverse weather and ambiguous signals to navigating multi-agent interactions on active railways, your work will directly shape the safety and reliability of our autonomous platform.

You’ll collaborate closely with top-tier engineers across autonomy, robotics, and systems, tackling some of the most challenging problems in real-time machine learning and computer vision. If you're excited by the opportunity to push the boundaries of AI in safety-critical, real-world applications, we’d love to work with you. 

This can be a remote role for a senior engineer with experience in 0 to 1 builds of perception systems. 

Responsibilities:

• 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 of production-grade models. 
• Build and optimize deep learning architectures for object detection, segmentation, tracking, pose estimation, and scene understanding. 
• Develop scalable and efficient training pipelines that ensure robust, real-time inference performance. 
• Work extensively with large image, video, lidar and radar datasets to power next-generation computer vision systems. 
• Conduct research and empirical studies to evaluate new architectures, techniques, and algorithmic improvements, incorporating or adapting state-of-the-art methods as appropriate. 
• Build and contribute to infrastructure and tools for supporting ML Pipeline to automate data labeling, training workflows, evaluation processes, and model versioning. 
• Collaborate cross-functionally with other engineering, research, and product teams to ensure seamless integration of ML systems into real-world applications. 

What Success Looks Like: 

• After 30 Days: You have developed a deep understanding of the current perception architecture, sensor setup, and system requirements. You've identified key challenges in the ML pipelines and proposed initial areas for improvement across data workflows, model performance, and deployment constraints. 
• After 60 Days: You’ve led the design of a new or improved perception subsystem and contributed hands-on to ML pipeline tooling. You've built a proof of concept aligned with system needs, demonstrating early improvements in performance or reliability based on real-world constraints. 
• After 90 Days: You have delivered a perception feature with a proven working model in offline testing, showing measurable gains. The system is integrated into the pipeline and is progressing toward edge deployment, with a clear impact on overall perception capabilities. 

Basic Requirements: 

• Bachelor’s or higher degree in Computer Science, Machine Learning, or a related technical discipline. 
• 4+ years of hands-on experience developing and deploying ML systems at scale. 
• Strong background in computer vision and/or deep learning with practical experience in designing and training neural networks for real-world applications. 
• Proficiency in Python and familiarity with standard ML libraries and tools (e.g., NumPy, SciPy, Pandas). 
• Expertise in at least one deep learning framework such as PyTorch or TensorFlow. 
• Strong mathematical foundation in linear algebra, geometry, probability, and optimization. 
• Proven track record of working autonomously and driving complex technical projects in fast-paced environments. 
• Excellent communication and collaboration skills, with experience working on interdisciplinary teams.  

Preferred Qualifications: 

• Experience with multi-modal perception (e.g., sensor fusion from cameras, lidar, radar). 
• Experience optimizing models for deployment on edge devices with real-time constraints. 
• Background in autonomous systems, robotics, or other safety-critical domains. 
• Publications in top-tier ML or CV conferences (e.g., CVPR, ICCV, NeurIPS, ICML, ECCV). 
• Experience with GPU/TPU programming and optimization tools (e.g., CUDA, TensorRT). 
• Knowledge of low-level programming languages like C++ or Rust. 
• Experience working directly with sensing hardware and understanding its constraints.