Research Engineer Internship

Avride • Austin, TX

15h • Onsite

About The Position

At Avride, Research 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 the ML Prediction and Planning team, which is responsible for building machine learning models that enable autonomous vehicles to understand their environment and make safe, efficient driving decisions on real roads. The team focuses on predicting the behavior of surrounding agents and generating trajectories that the vehicle can follow in complex, dynamic scenarios. You will be paired with a dedicated senior researcher 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 from a research paper, prototype it, and evaluate its performance in a complex, safety-critical system. We are currently offering two different internships within our ML Prediction and Planning team for the Summer of 2026.

Requirements

• 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.
• Candidates are required to be authorized to work in the U.S.

Nice To Haves

• Pursuing a PhD is highly preferred

Responsibilities

• Take ownership of a research project focused on exploring how model ensembling strategies influence the gap between open-loop (training) and closed-loop (simulation) performance.
• Review relevant literature, formulate hypotheses, and prototype solutions using Python and ML frameworks (like PyTorch).
• Implement and evaluate multiple ensembling approaches, including blending models trained with different random seeds, combining checkpoints from different training stages, and applying weighted averaging or learned blending of model outputs.
• Systematically compare single-model vs ensemble performance and seed diversity vs checkpoint diversity, and measure their impact on open-loop metrics (training/validation loss, accuracy) and closed-loop metrics (simulation performance, safety, stability).
• Investigate the correlation (or lack thereof) between open-loop and closed-loop improvements, identify cases where ensembling improves one metric but degrades the other, and formulate hypotheses explaining the observed behavior.
• Work on evaluating and improving the behavior of ML-driven traffic agents in our autonomous driving simulator.
• Design evaluation functions that select trajectories with desired properties — from realistic to adversarial — and build quantitative metrics to measure how agent behavior changes.
• Design, test, and iterate algorithms that select agent trajectories optimizing for different objectives: aggressiveness, interaction density, route fidelity.
• Build evaluation metrics for comparing agent behavior strategies: interaction intensity (time-to-collision, proximity), kinematics plausibility (acceleration, jerk), and distributional similarity to real traffic.
• Run experiments on large-scale scenario pools, comparing ML agents against baseline approaches and measuring the impact of different strategies.
• Work with production codebase, specifically a C++ simulation pipeline running large-scale scenario evaluation.
• Conclude the internship by presenting methodology, experimental results, and data-driven recommendations on where trajectory ranking is sufficient and where model-level changes are required.

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