About the job
Join Our Innovative Team
At avride, we are pioneers in developing cutting-edge autonomous vehicle and delivery robot technology. With the recent launch of our robotaxi service in Dallas, we are not just keeping pace with innovation; we are defining the future of mobility. Our dedicated team is focused on constructing self-driving solutions from the ground up, utilizing machine learning as the backbone of our development process to ensure safe and intelligent navigation. We deploy state-of-the-art models that tackle critical challenges in autonomous systems, employing advanced deep learning architectures such as Convolutional Neural Networks (CNNs), Transformers, and Multimodal Large Language Models (MLLMs). These technologies power both on-board and off-board applications, delivering robust and efficient operations. Your contributions will directly enhance the performance, safety, and reliability of our autonomous vehicles and delivery robots.
About the Role
We are seeking experienced Machine Learning Engineers at Senior, Staff, and Principal levels to join our team in Austin, Texas. Whether you are a strong individual contributor eager to tackle complex technical challenges or an established technical leader looking to make a significant impact, we would love to hear from you.
In this role, you will spearhead the development and deployment of machine learning solutions for some of the most challenging problems in autonomy. This will involve conducting experiments, managing large-scale datasets, and implementing deep learning models specifically designed for real-world autonomous systems. At senior levels, you will also define technical strategies, mentor junior engineers, and influence the practice of machine learning across the organization.
Your Key Responsibilities
- Model Development and Optimization: Design, implement, and refine deep learning models to guarantee efficiency, scalability, and robustness. This includes models for environmental perception and predicting road user behavior. At Staff and Principal levels, you will establish the technical vision for entire model families and guide architectural decisions across teams.
- Dataset Management: Oversee data collection, preprocessing, and augmentation to uphold high-quality datasets for training and evaluation. Senior-level engineers will set standards and tooling that can be scaled across the organization.
- Training Pipeline Enhancement: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring. Staff and Principal engineers will own the long-term roadmap for training infrastructure.
- Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.