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New Grad Quant Jobs in Texas (NOW HIRING)

Meds

Entry Level Data Analyst

Austin, TX

As we continue our accelerated growth trajectory, we're launching new products to expand our ... This is for recent grads or soon to be graduates only (Grad Dates : May 2026 - December 2026) who ...

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New Grad Quant information

What is the difference between New Grad Quant vs Quant Analyst?

AspectNew Grad QuantQuant Analyst
Required CredentialsDegree in Math, Finance, or Computer Science; internships preferredAdvanced degree often preferred; experience in modeling and programming
Work EnvironmentEntry-level, training-focused, collaborative teamsMore independent, project-driven, client-facing
Employer & Industry UsageFinancial firms, hedge funds, banksSame as New Grad Quant, with increased responsibilities

The main difference between a New Grad Quant and a Quant Analyst lies in experience and responsibility. New Grad Quants are entry-level, focusing on learning and supporting teams, while Quant Analysts have more experience, handling complex models and client interactions. Both roles require strong quantitative skills, but the Quant Analyst role typically demands a deeper understanding and proven track record.

What are some typical challenges new graduate quants face when transitioning from academia to a professional finance environment?

New graduate quants often encounter challenges such as adapting to the fast-paced nature of financial markets, learning to apply theoretical knowledge to real-time problems, and navigating large codebases or proprietary platforms. Collaboration is key, as quants frequently work in cross-functional teams with traders, developers, and risk managers, requiring strong communication skills. Additionally, new grads must prioritize continuous learning to keep up with evolving models and technologies, while managing deadlines and performance expectations.

What is a New Grad Quant?

A New Grad Quant, short for 'Quantitative Analyst,' is an entry-level position for recent graduates in finance, mathematics, computer science, or related fields. These professionals use mathematical models, statistical techniques, and programming skills to analyze financial data and develop trading strategies. New Grad Quants typically work at investment banks, hedge funds, or financial technology firms, where they support senior quants in research, risk management, and portfolio optimization. The role often involves a steep learning curve and requires strong analytical thinking and problem-solving abilities.

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

To thrive as a New Grad Quant, you need a strong background in mathematics, statistics, and programming, typically supported by a degree in quantitative fields such as mathematics, physics, engineering, or computer science. Proficiency with tools like Python, R, MATLAB, and familiarity with data analysis libraries or financial modeling systems is expected. Analytical thinking, attention to detail, and effective communication are crucial soft skills for collaborating with teams and interpreting complex data. These competencies enable new quants to develop robust models, contribute valuable insights, and adapt quickly in the fast-paced finance industry.
What are popular job titles related to New Grad Quant jobs in Texas? For New Grad Quant jobs in Texas, the most frequently searched job titles are:
What job categories do people searching New Grad Quant jobs in Texas look for? The top searched job categories for New Grad Quant jobs in Texas are:
What cities in Texas are hiring for New Grad Quant jobs? Cities in Texas with the most New Grad Quant job openings:
New Grad Quant jobs near you
Infographic showing various New Grad Quant job openings in Texas as of July 2026, with employment types broken down into 1% Internship, 89% Full Time, 9% Part Time, and 1% Contract. Highlights an 75% Physical, and 25% Remote job distribution.

Algorithm Engineer, Deep Learning & Vision (New Grad)

Bot Auto

Houston, TX • On-site

Full-time

Posted 3 days ago

New

Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
Key Responsibilities
  • Model Implementation & Iteration: Participate in the development, training, and optimization of state-of-the-art deep learning models for autonomous driving, with a focus on end-to-end architectures, including perception, online mapping, and end-to-end planning.
  • Full Lifecycle Execution: Engage in the entire machine learning workflow under the guidance of domain experts, spanning from data curation and data analysis to model experimentation, hyperparameter tuning, and rigorous performance metric verification.
  • Cross-Functional Collaboration: Partner with simulation, infrastructure, and downstream planning/control teams to deploy, evaluate, and integrate machine learning components into our production pipeline for autonomous trucks.
  • Literature Tracking: Stay abreast of the latest research breakthroughs in computer vision and generative AI, and actively bench-test promising SOTA methods to solve real-world corner cases.
How You'll Grow
This matters as much to us as what you'll ship.
  • You get a real mentor. Every engineer is paired with senior-level engineers developing you. Mentorship here is weighted toward design and judgment: how to frame a problem, what to build and why, how to tell whether a solution is actually right.
  • We promote fast. Managers are expected to push engineers to attempt work above their current level, and to promote in the next cycle when they deliver it.
Qualifications
Required:
  • Education: A Bachelor's, Master's, or Ph.D. (including upcoming graduates) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
  • You have trained neural networks. Coursework, research, personal projects, open-source work, and internships all count. We care that you have actually run the loop: built a model, trained it, found out why it was not working, and fixed it.
  • Core Knowledge: Strong theoretical foundation in machine learning and deep learning, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).
  • Technical Stack: Proficiency in Python and deep learning frameworks such as PyTorch, along with strong software engineering fundamentals (data structures, algorithms, and clean coding practices).
  • Attributes: High self-motivation, strong analytical and problem-solving skills, a fast learner in a high-velocity startup environment, and a strong team-player mindset.
Preferred:
  • Computer vision. Research or projects in computer vision, and particularly in 3D.
  • Specific Research Directions: Academic thesis or deeply focused research experience in one or more of the following domains:
    • Computer Vision (2D or 3D)
    • Online Mapping, Vectorization, or Visual SLAM
    • Prediction and Behavioral Modeling
  • Academic Achievements: A track record of research publications in machine learning, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS).
  • Engineering Plus: Hands-on experience with model deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++).
  • Industry Exposure: Prior internship experience within the autonomous driving industry or advanced robotics labs.

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