Machine Learning Engineer Internship at Avride in Austin, TX

Summary: Avride is a US-based developer of autonomous vehicles and delivery robots, and they are seeking Machine Learning Engineer Interns to work within their Perception team. Interns will leverage massive datasets to train models, develop algorithms, and solve real-world challenges in autonomous driving, while being mentored by experienced professionals. Responsibilities: - Research how to leverage the broad visual knowledge of pre-trained, open-source 2D models for 3D applications - Design and run rigorous experiments in our simulation environment to prove your models can detect rare, infrequent objects without sacrificing precision - Work closely with your mentor to prototype and iterate on techniques that adapt these 2D features into our current perception stack - Conclude your internship by sharing your experimental findings, recall/precision trade-offs, and simulation methodology with the research and engineering groups - Lead a scoped research initiative to advance our 3D perception capabilities - Dive into state-of-the-art literature on RGB-only methods and formulate hypotheses to improve sensor fusion - Utilize Avride’s extensive real-world LiDAR and camera datasets to train, test, and evaluate ML models using PyTorch, aiming to extract stronger, more reliable signals from RGB data - Partner with your mentor to design and refine algorithms that directly enhance our existing perception baselines - Present your methodology, fusion results, and future recommendations to the broader engineering and research teams at the end of your term - Own the development of a new vector-based search capability to upgrade how we query our scene database - Research and integrate embedding models (like CLIP) alongside our existing natural language systems - Build out the backend infrastructure using Python to map and search Avride's massive library of real-world camera data - Collaborate with your mentor to deploy these embedding models effectively, unlocking faster and smarter data mining for our labeling and perception teams - Present your system architecture, search performance metrics, and the practical impact of your new tool to the wider engineering organization - Lead an applied engineering project centered on our vehicle microphone arrays - Design and build a robust data mining pipeline to extract relevant audio signals from raw vehicle logs - Leverage large open-source models to automatically label your mined data, then use that dataset to train and fine-tune a compact, efficient onboard ML model for siren recognition - Partner with your mentor to iterate on the model's performance, ensuring it is highly accurate and lightweight enough for real-time onboard processing - Wrap up your internship by demoing your automated labeling pipeline and the performance of your onboard siren detector to the engineering teams Required Qualifications: - Currently pursuing a Master's or PhD (highly preferred) in Computer Science, Robotics, Machine Learning, Applied Mathematics, or a related field with an expected graduation date between Winter 2026 and Spring 2027 - Strong understanding of deep learning, reinforcement learning, computer vision, optimization, or probabilistic modeling - Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow) - Basic familiarity or willingness to learn C++ - Ability to read, understand, and implement algorithms from academic research papers - A strong analytical mindset for designing experiments and interpreting data - Highly collaborative, open to feedback, and excited to tackle unsolved problems in the autonomous driving space Required Skills: Machine Learning, Deep Learning, Reinforcement Learning, Computer Vision, Optimization, Probabilistic Modeling, Python, PyTorch, TensorFlow, C++, Research Paper Implementation, Experiment Design, Openness to feedback Benefits: 1:1 Mentorship, Massive Compute & Data, Networking & Culture