Machine Learning Engineer Internship

Avride • Austin, TX

4h • Onsite

About The Position

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.

Requirements

• Currently pursuing a Master’s or PhD 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.
• Candidates are required to be authorized to work in the U.S.

Nice To Haves

• Currently pursuing a PhD (highly preferred) in Computer Science, Robotics, Machine Learning, Applied Mathematics, or a related field.

Responsibilities

• Research how to leverage the broad visual knowledge of pre-trained, open-source 2D models for 3D applications to solve long-tail entity recognition.
• 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.
• Share 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.
• Design and build a robust data mining pipeline to extract relevant audio signals from raw vehicle logs centered on our vehicle microphone arrays.
• Leverage large open-source models to automatically label your mined data.
• Train and fine-tune a compact, efficient onboard ML model for siren recognition using the automatically labeled dataset.
• Partner with your mentor to iterate on the model's performance, ensuring it is highly accurate and lightweight enough for real-time onboard processing.
• Demo your automated labeling pipeline and the performance of your onboard siren detector to the engineering teams.

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Number of Employees: 1-10 employees