About the role
<h2 class="heading-2">About the team</h2> <p>Avride develops autonomous vehicle and delivery robot technology, leveraging deep expertise in autonomous systems. With the recent launch of our robotaxi service in Dallas, we are accelerating innovation and redefining the future of mobility. Our team builds self-driving solutions from the ground up, with machine learning at the core of our development pipeline to enable safe and intelligent navigation. We design and deploy state-of-the-art models to address key challenges in autonomous systems, utilizing advanced deep learning architectures such as Convolutional Neural Networks (CNNs), Transformers, and Multimodal Large Language Models (MLLMs). These models power both onboard and offboard applications, ensuring robust and efficient operation. Your work will directly contribute to enhancing the performance, safety, and reliability of Avride's autonomous vehicles and delivery robots.</p> <h2>About the role</h2> <p>We are hiring experienced Machine Learning Engineers across Senior, Staff, and Principal levels. to site onsite in Austin, Texas. Whether you're a strong individual contributor ready to take on complex technical challenges, or a seasoned technical leader looking to take on complex technical challenges we want to hear from you.<br><br>In this role, you will drive the development and deployment of machine learning solutions for some of the hardest problems in autonomy conducting experiments, managing large-scale datasets, and implementing deep learning models tailored for real-world autonomous systems. At more senior levels, you will also define technical strategy, mentor engineers, and influence how ML is practiced across the organization.</p> <h2>What you'll do</h2> <ul> <li>Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness — including models for environmental perception and predicting the behavior of other road users. At Staff and Principal levels, you will set the technical vision for entire model families and drive architectural decisions across teams.</li> <li>Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation. Senior+ engineers will establish standards and tooling that scale across the organization.</li> <li>Enhance and Maintain Training Pipelines: 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.</li> <li>Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.</li> <