Senior Machine Learning/Computer Vision Engineer

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. They are committed to diversity and inclusion, ensuring reasonable accommodations for individuals with disabilities.

Introduction

Parallel Systems is revolutionizing freight transportation with autonomous battery-electric rail vehicles. We aim to shift a significant portion of the $900 billion U.S. trucking industry to rail, offering cleaner, safer, and more efficient logistics. Join our team to contribute to a greener future for global freight.

As a Senior Machine Learning Engineer specializing in Computer Vision, you will be instrumental in developing the next generation of perception systems for our fully autonomous, battery-electric rail vehicles. You will lead the design and deployment of cutting-edge deep learning models, enabling our vehicles to accurately perceive and understand complex, real-world environments, including challenges like adverse weather, ambiguous signals, and multi-agent interactions on active railways. Your work will directly impact the safety and reliability of our autonomous platform.

This role involves close collaboration with top-tier engineers in autonomy, robotics, and systems, tackling intricate problems in real-time machine learning and computer vision. If you are passionate about pushing the boundaries of AI in safety-critical, real-world applications, we encourage you to apply. This is a remote opportunity for a senior engineer experienced in building perception systems from scratch.

Responsibilities

• Design, develop, and deploy advanced machine learning models for large-scale perception problems.
• Own the complete 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 to 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, adapting state-of-the-art methods as needed.
• Build and contribute to infrastructure and tools supporting the ML Pipeline to automate data labeling, training workflows, evaluation processes, and model versioning.
• Collaborate cross-functionally with other engineering, research, and product teams to integrate ML systems seamlessly into real-world applications.

What Success Looks Like

• After 30 Days: You will have a deep understanding of the current perception architecture, sensor setup, and system requirements. You will have 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 will have led the design of a new or improved perception subsystem and contributed hands-on to ML pipeline tooling. You will have 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 will have delivered a perception feature with a proven working model in offline testing, demonstrating measurable gains. The system will be integrated into the pipeline and progressing toward edge deployment, with a clear impact on overall perception capabilities.

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.

About Company

Parallel Systems is an equal opportunity employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to any discriminatory factor protected by applicable federal, state or local laws. We work to build an inclusive environment in which all people can come to do their best work. Parallel Systems is committed to the full inclusion of all qualified individuals. As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.