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Avride is a US-based developer of autonomous vehicles and delivery robots. We develop and operate both autonomous cars and delivery robots that share technologies and mutually benefit from each other's advancements-a unique approach in the industry. 

At Avride, ML Engineer Interns operate at the intersection of cutting-edge academic research and real-world engineering. You will use our massive datasets of real driving logs to train models and develop algorithms.

During this internship, you will be embedded in our Perception team. The Perception team serves as the eyes and ears of our autonomous vehicles, transforming raw data from cameras, LiDAR, and microphones into a precise, real-time 3D understanding of the surrounding world. 

You will be paired with a dedicated senior mentor and work on problems directly impacting real-world driving performance. This program is designed to give you a deep understanding of how to take a theoretical concept or novel system architecture, prototype it, and evaluate its performance within a complex, safety-critical stack.

We are currently offering four different internships within our Perception Team for the Summer of 2026. 

Long-Tail 3D Entity Recognition via Pre-Trained 2D Models

• Targeted ML Investigation: Take charge of solving a classic autonomous driving challenge: long-tail entity recognition. You will research how to leverage the broad visual knowledge of pre-trained, open-source 2D models for 3D applications.
• Simulation-Driven Evaluation: Design and run rigorous experiments in our simulation environment to prove your models can detect rare, infrequent objects without sacrificing precision.
• Feature Integration: Work closely with your mentor to prototype and iterate on techniques that adapt these 2D features into our current perception stack.
• Knowledge Sharing: Conclude your internship by sharing your experimental findings, recall/precision trade-offs, and simulation methodology with the research and engineering groups.

RGB-Only 3D Perception & RGB-LiDAR Fusion

• Applied Research Ownership: Lead a scoped research initiative to advance our 3D perception capabilities. You will dive into state-of-the-art literature on RGB-only methods and formulate hypotheses to improve sensor fusion.
• Model Training & Experimentation: 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.
• Iterative Prototyping: Partner with your mentor to design and refine algorithms that directly enhance our existing perception baselines.
• Knowledge Sharing: Present your methodology, fusion results, and future recommendations to the broader engineering and research teams at the end of your term.

Data Engineering - Visual Scene Search via Vector Embeddings

• System Architecture & Design: Own the development of a new vector-based search capability to upgrade how we query our scene database. You will research and integrate embedding models (like CLIP) alongside our existing natural language systems.
• Data Tooling Implementation: Build out the backend infrastructure using Python to map and search Avride's massive library of real-world camera data.
• Pipeline Integration: Collaborate with your mentor to deploy these embedding models effectively, unlocking faster and smarter data mining for our labeling and perception teams.
• Knowledge Sharing: Present your system architecture, search performance metrics, and the practical impact of your new tool to the wider engineering organization.

Audio Signal Processing & Siren Recognition Pipeline

• End-to-End Pipeline Creation: Lead an applied engineering project centered on our vehicle microphone arrays. You will design and build a robust data mining pipeline to extract relevant audio signals from raw vehicle logs.
• Auto-Labeling & Fine-Tuning: 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.
• Edge Optimization: Partner with your mentor to iterate on the model's performance, ensuring it is highly accurate and lightweight enough for real-time onboard processing.
• Knowledge Sharing: Wrap up your internship by demoing your automated labeling pipeline and the performance of your onboard siren detector to the engineering teams.

• Education: Currently pursuing a Bachelor's, 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. 
• Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning, computer vision, optimization, or probabilistic modeling.
• Programming Skills: Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow). Basic familiarity or willingness to learn C++.
• Research Acumen: Ability to read, understand, and implement algorithms from academic research papers. A strong analytical mindset for designing experiments and interpreting data.
• Eagerness to Learn: Highly collaborative, open to feedback, and excited to tackle unsolved problems in the autonomous driving space.

• 1:1 Mentorship: Direct guidance from leading researchers and engineers in the autonomous vehicle industry to help you navigate technical roadblocks and grow your career.
• Massive Compute & Data: Access to state-of-the-art driving data to fuel your experiments.
• Networking & Culture: Invitations to tech talks, paper reading groups, intern social events, and cross-team collaborations.

Please note that this is an in-person internship based at our office in Austin, Texas.  We are prioritizing candidates who currently reside within commuting distance of Austin.  We do not provide relocation assistance, travel reimbursement, or housing stipends for this position.