Apprentice Machine Learning Testing Jobs in Cedar Park, TX

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.

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.
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.

• Develop and Optimize Machine Learning Models: 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.
• Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation.
• Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring.
• Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
• Explore and Apply Cutting-Edge ML Techniques: Stay up to date with advancements in deep learning and experiment with novel approaches to improve model performance.
• Collaborate with Cross-Functional Teams: Work closely with researchers, software engineers, and robotics experts to integrate machine learning solutions into real-world autonomous systems.

• Strong understanding of fundamental machine learning algorithms and neural network techniques.
• Expertise in at least one modern machine learning domain, such as computer vision, large language models, or generative AI.
• At least three years of experience developing neural network-based algorithms, including data collection, training, and deployment.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, along with PySpark, NumPy, and SciPy.
• Working knowledge of C++ and SQL.
• Ability to quickly absorb new concepts by reviewing research papers, technical reports, and documentation.
• Strong collaboration and communication skills, with the ability to align technical work with business objectives and drive results.

• Advanced degree in Computer Science, Machine Learning, Robotics, or a related field.
• Experience developing ML algorithms for autonomous vehicles or robotics applications.
• Familiarity with neural network deployment and optimization tools such as triton, TensorRT, or similar frameworks.
• Proven ability to set and achieve mid- and long-term goals, prioritize tasks, and meet deadlines independently.
• Experience working in cross-functional teams within a multidisciplinary environment.
• Publications in top-tier ML conferences or contributions to patent applications or ML-related open-source projects.