About the Team

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.

About the Role

We are hiring experienced Machine Learning Engineers across Senior, Staff, and Principal levels. to site onsite in Austin, Texas. Whether you're a strong individual contributor ready to take on complex technical challenges, or a seasoned technical leader looking to take on complex technical challenges we want to hear from you.

In this role, you will drive the development and deployment of machine learning solutions for some of the hardest problems in autonomy conducting experiments, managing large-scale datasets, and implementing deep learning models tailored for real-world autonomous systems. At more senior levels, you will also define technical strategy, mentor engineers, and influence how ML is practiced across the organization.

What You'll Do

• Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness — including models for environmental perception and predicting the behavior of other road users. At Staff and Principal levels, you will set the technical vision for entire model families and drive architectural decisions across teams.
• Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation. Senior+ engineers will establish standards and tooling that scale across the organization.
• Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring. Staff and Principal engineers will own the long-term roadmap for training infrastructure.
• Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
• Explore and Apply Cutting-Edge ML Techniques: Stay current with advancements in deep learning and lead the evaluation and adoption of novel approaches. Principal engineers are expected to identify opportunities before they become industry standard.
• Collaborate and Lead Across Teams: Work closely with researchers, software engineers, and robotics experts to integrate ML into real-world autonomous systems. At Staff and Principal levels, you will drive alignment across functions, mentor junior and senior engineers, and serve as a technical authority across the org.

What You'll Need

• Strong understanding of fundamental machine learning algorithms and neural network techniques.
• Deep expertise in at least one modern ML domain, such as computer vision, large language models, or generative AI.
• Senior: 4+ years of experience developing neural network-based algorithms, including data collection, training, and deployment.
• Staff: 7+ years of experience, with a track record of leading significant technical initiatives and influencing engineering practices beyond your immediate team.
• Principal: 10+ years of experience, with demonstrated impact at an organizational or industry level — setting multi-year technical direction and driving outcomes across multiple teams.
• 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 from research papers, technical reports, and documentation.
• Strong collaboration and communication skills, with the ability to align technical work with business objectives at all levels of the organization.

What You Must Have

• 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.
• Publications in top-tier ML conferences, contributions to patent applications, or ML-related open-source projects.
• For Staff/Principal: experience building and scaling ML teams, defining org-wide technical standards, or driving cross-company research agendas

About Avride

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. 

About the Internship

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.

What You’ll Do

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.

What You’ll Need

• 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.

What You’ll Get

• 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.

About the Team

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.

About the Role

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.

What You'll Do

• 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.

What You'll Need

• 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.

Nice to Have

• 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.