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Quant Developer Internship Jobs in Texas (NOW HIRING)

Avride

Research Engineer Internship

Austin, TX · On-site

About Avride Avride is a US-based developer of autonomous vehicles and delivery robots. We develop ... and build quantitative metrics to measure how agent behavior changes. Today we assess realism ...

Avride

Research Engineer Internship

Austin, TX · On-site

About Avride Avride is a US-based developer of autonomous vehicles and delivery robots. We develop ... and build quantitative metrics to measure how agent behavior changes. Today we assess realism ...

Copart, Inc.

Software Engineering Intern

Dallas, TX · On-site

We are looking for passionate Software Engineering Interns with a strong interest in Artificial ... quantitative field. AI & LLM Foundational Knowledge: Hands-on experience or academic projects ...

Copart, Inc.

Software Engineering Intern

Dallas, TX · On-site

We are looking for passionate Software Engineering Interns with a strong interest in Artificial ... quantitative field. AI & LLM Foundational Knowledge: Hands-on experience or academic projects ...

Copart, Inc.

Data & AI Intern

Dallas, TX · On-site

We are looking for passionate Software Engineering Interns with a strong interest in Artificial ... quantitative field. AI & LLM Foundational Knowledge: Hands-on experience or academic projects ...

JANSON

Junior Data Analyst / Developer

Fayetteville, TX · On-site

The Junior Analyst / Developer will provide analytical and instructional support while developing ... quantitative field · 0-2 years of relevant experience (including internships, labs, or academic ...

JANSON

Junior Data Analyst / Developer

Fayetteville, TX · On-site

The Junior Analyst / Developer will provide analytical and instructional support while developing ... quantitative field · 0-2 years of relevant experience (including internships, labs, or academic ...

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Quant Developer Internship information

What is a Quant Developer Internship?

A Quant Developer Internship is a temporary position, usually for students or recent graduates, that offers hands-on experience in quantitative finance and software development. Interns work with quantitative analysts and developers to design, implement, and optimize financial models and trading algorithms. The role typically involves programming, data analysis, and collaborating with other teams to solve real-world financial problems. This internship is an excellent opportunity for those interested in combining finance, mathematics, and computer science in a professional setting.

What is the difference between Quant Developer Internship vs Quant Analyst Internship?

AspectQuant Developer InternshipQuant Analyst Internship
Required CredentialsTypically pursuing or holding a degree in Computer Science, Mathematics, or related fieldsUsually pursuing or holding a degree in Finance, Economics, or related fields
Work EnvironmentHands-on coding, software development, and algorithm implementationData analysis, financial modeling, and strategy development
Employer & Industry UsageUsed in hedge funds, investment banks, and trading firms focusing on technology-driven rolesCommon in asset management firms, hedge funds, and financial institutions focusing on market analysis

While both internships involve finance and quantitative skills, Quant Developer Internships focus more on programming and software development, whereas Quant Analyst Internships emphasize financial analysis and modeling. Candidates should choose based on their strengths in coding versus financial analysis.

What are some common challenges faced during a Quant Developer Internship, and how can interns overcome them?

Quant Developer Interns often encounter challenges such as adapting to complex financial models, working with large datasets, and mastering specialized programming languages like Python or C++. To overcome these, interns should proactively seek guidance from senior team members, participate in regular code reviews, and allocate time to strengthen their understanding of both financial concepts and software development best practices. Collaboration and open communication within the team are crucial for navigating technical obstacles and successfully delivering project tasks.

What are the key skills and qualifications needed to thrive as a Quant Developer Intern, and why are they important?

To thrive as a Quant Developer Intern, you need a strong background in mathematics, statistics, and programming, typically demonstrated through a degree in quantitative fields like computer science, mathematics, or engineering. Familiarity with programming languages such as Python, C++, or Java, and experience using financial modeling tools or libraries are highly valued. Analytical thinking, attention to detail, and effective communication are critical soft skills for collaborating with teams and interpreting complex data. These skills and qualities are essential for developing robust quantitative models and contributing effectively to quantitative research and trading strategies.
What are the most commonly searched types of Quant Developer jobs in Texas? The most popular types of Quant Developer jobs in Texas are:
What are popular job titles related to Quant Developer Internship jobs in Texas? For Quant Developer Internship jobs in Texas, the most frequently searched job titles are:
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Quant Developer Internship jobs near you

Research Engineer Internship

Avride

Austin, TX • On-site

Internship

Posted 17 days ago

Job description

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, 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.
What You'll Do
We are currently offering two different internships within our ML Prediction and Planning team for the Summer of 2026.
Autonomous Vehicles
  • Applied Research Project: 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. You will review relevant literature, formulate hypotheses, and prototype solutions using Python and ML frameworks (like PyTorch).
  • Design Ensembling Strategies: 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.
  • Run Controlled Experiments: 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).
  • Analyze Metric Alignment: 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.

Simulation
  • Applied Research Project: You will work on evaluating and improving the behavior of ML-driven traffic agents in our autonomous driving simulator. Our prediction model generates multiple trajectory candidates for each simulated agent at every step. Your job is to design evaluation functions that select trajectories with desired properties - from realistic to adversarial - and build quantitative metrics to measure how agent behavior changes. Today we assess realism visually; you will replace that with data-driven evaluation that becomes the standard tool for measuring every future improvement to our agent simulation. You'll work with real driving data, run experiments on large scenario pools, and produce results that directly influence the team's roadmap for agent simulation.
  • Design and implement algorithms: work alongside your mentor to 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.
  • Data-Driven Experimentation: run experiments on large-scale scenario pools, comparing ML agents agains baseline approaches and measuring the impact of different strategies.
  • Work with production codebase: the prediction models you'll experiment with are the same ones deployed in our autonomous vehicles. Your work is a part of a C++ simulation pipeline running large-scale scenario evaluation.
  • Knowledge Sharing: Conclude your internship by presenting your methodology, experimental results, and data-driven recommendations on where trajectory ranking is sufficient and where model-level changes are required.
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.
Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

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