Machine Learning Engineer Jobs in Austin, USA

<h2 class="heading-2">About the team</h2> <p class="p1">Our team develops the core software and data processing systems that power motion planning and decision-making in autonomous vehicles. We work at the intersection of machine learning, large-scale data infrastructure, and real-time vehicle control, collaborating across engineering, analytics, and product teams to deliver safe and intelligent driving capabilities.</p> <h2 class="heading-2">About the role</h2> <p class="p1">We are looking for a creative & driven Machine Learning Engineer to join our autonomous vehicle team. You will be at the center of our efforts to build intelligent systems that can understand, predict, and safely navigate a complex and dynamic world. This role involves designing and training the next generation of deep learning models that form the brain of our vehicle, learning from petabytes of real-world driving data. If you are passionate about applying cutting-edge ML to solve high-stakes robotics challenges, we want to hear from you.</p> <h2 class="heading-2">What you'll do</h2> <ul> <li>Design, train, and deploy state-of-the-art machine learning models for behavioral prediction and motion planning</li> <li>Develop robust data pipelines to process, clean, and label massive-scale vehicle sensor and simulation datasets</li> <li>Work with deep learning architectures such as transformers to model complex temporal interactions between traffic agents</li> <li>Establish and own the metrics for model performance, and create evaluation frameworks that correlate with on-road safety and performance</li> <li>Collaborate with software engineers to integrate and optimize trained models for real-time inference on the vehicles embedded hardware</li> <li>Stay current with the latest research in machine learning, imitation learning, and reinforcement learning, and apply novel techniques to our systems</li> </ul> <h2 class="heading-2">What you'll need</h2> <ul> <li>Strong proficiency in Python and hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX)</li> <li>Solid understanding of machine learning fundamentals, including various neural network architectures, training methodologies, and evaluation techniques</li> <li>Experience with the full machine learning lifecycle, from data exploration and prototyping to deployment and monitoring</li> <li>Proficiency in C++ for writing high-performance model inference code</li> </ul> <h2 class="heading-2">Nice to have</h2> <ul> <li>A strong track record in ML competitions (e.g., Kaggle) or contributions to major open-source ML projects</li> <li>Experience applying ML to problems

<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 <strong>launch of our robotaxi service in Dallas</strong>, we are accelerating innovation and redefining the future of mobility.</p> <p>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 class="heading-2">About the role</h2> <p>We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In this role, you will conduct experiments, manage large-scale datasets, and implement deep learning models tailored for autonomous systems.<br><br>You will utilize cloud platforms, orchestration tools, and machine learning frameworks to develop scalable and efficient solutions. Additionally, you will analyze the latest research, assess the applicability of emerging deep learning techniques, and drive innovation in autonomous vehicle technology.</p> <h2 class="heading-2">What you'll do</h2> <ul> <li><strong>Develop and Optimize Machine Learning Models:</strong> Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness. This may include developing models for understanding a self-driving vehicle’s surroundings or predicting the intentions of other road users.</li> <li><strong>Curate and Manage Large-Scale Datasets:</strong> Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation.</li> <li><strong>Enhance and Maintain Training Pipelines:</strong> Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring.</li> <li><strong>Improve Model Deployment and Efficiency:</strong> Optimize inference performance, model compression, and deployment across various hardware platforms.</li> <li><strong>Explore and Apply Cutting-Edge ML Techniques:</strong> Stay up to date with advancements in deep le

<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> &lt