Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Their innovative technology offers cleaner, safer, and more efficient logistics solutions, aiming to shape a smarter, greener future for global freight.

About the Position

Introduction

Parallel Systems is seeking an experienced Senior Machine Learning/Computer Vision Engineer to join their team. You will be instrumental in developing the next generation of perception systems for fully autonomous, battery-electric rail vehicles. This role involves designing and deploying cutting-edge deep learning models to enable vehicles to perceive and interpret complex, real-world environments, ensuring safety and reliability in critical applications.

Responsibilities

Design, develop, and deploy advanced machine learning models for large-scale perception challenges.
Manage the entire 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 for robust, real-time inference performance.
Work extensively with extensive image, video, LiDAR, and radar datasets for next-generation computer vision systems.
Conduct research and empirical studies to evaluate and incorporate state-of-the-art architectures, techniques, and algorithmic improvements.
Build and contribute to infrastructure and tools supporting the ML Pipeline, automating data labeling, training workflows, evaluation processes, and model versioning.
Collaborate cross-functionally with engineering, research, and product teams to integrate ML systems seamlessly into real-world applications.

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.

Nice to Have

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.

What Success Looks Like

After 30 Days: Develop a deep understanding of the current perception architecture, sensor setup, and system requirements. Identify key challenges in ML pipelines and propose initial areas for improvement across data workflows, model performance, and deployment constraints.
After 60 Days: Lead the design of a new or improved perception subsystem and contribute hands-on to ML pipeline tooling. Build a proof of concept aligned with system needs, demonstrating early improvements in performance or reliability based on real-world constraints.
After 90 Days: Deliver 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.