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Research Assistant Deep Learning Jobs in Prairie View, TX

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Research Assistant Deep Learning information

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How much do research assistant deep learning jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for research assistant deep learning in Prairie View, TX is $19.76, according to ZipRecruiter salary data. Most workers in this role earn between $16.68 and $22.98 per hour, depending on experience, location, and employer.

What is a research assistant deep learning?

Research Assistant Deep Learning jobs involve supporting research projects focused on artificial intelligence, specifically within the field of deep learning. These roles typically require assisting with data collection, preprocessing, running machine learning experiments, and analyzing results. Research assistants may also help with literature reviews, code development, and documentation. The position is often found in academic, industry, or research lab settings, and usually requires a solid foundation in programming, mathematics, and neural network concepts.

What does a research assistant deep learning do?

As a Research Assistant in Deep Learning, you can expect to work closely with research scientists and engineers to design, implement, and evaluate novel deep learning models. Typical daily tasks include data preprocessing, running experiments, analyzing results, and contributing to academic papers or presentations. You may also assist in developing codebases, conducting literature reviews, and collaborating with team members to solve technical challenges. The work environment is often collaborative and fast-paced, with opportunities to learn from experts and contribute to cutting-edge research projects.

What are the key skills and qualifications needed to thrive as a research assistant deep learning?

To thrive as a Research Assistant in Deep Learning, you need a strong background in machine learning, programming (especially Python), and a relevant degree in computer science or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience with data preprocessing and GPU computing, are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills and qualities are essential for efficiently developing, testing, and improving advanced machine learning models in a fast-evolving field.

What is the difference between Research Assistant Deep Learning vs Research Assistant Machine Learning?

AspectResearch Assistant Deep LearningResearch Assistant Machine Learning
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of neural networksBachelor's or Master's in Computer Science, Data Science, or related fields; foundational ML knowledge
Work EnvironmentResearch labs, universities, tech companies focusing on AI and neural networksResearch labs, universities, tech companies working on various ML algorithms
Employer & Industry UsageAI research, deep learning projects, neural network developmentGeneral machine learning applications, data analysis, predictive modeling

Research Assistant Deep Learning specializes in neural networks and AI-focused projects, while Research Assistant Machine Learning covers a broader range of algorithms and data analysis tasks. Both roles require similar educational backgrounds but differ in technical focus and application areas.

What job categories do people searching Research Assistant Deep Learning jobs in Prairie View, TX look for?

The top searched job categories for Research Assistant Deep Learning jobs in Prairie View, TX are:

What cities near Prairie View, TX are hiring for Research Assistant Deep Learning jobs?

Cities near Prairie View, TX with the most Research Assistant Deep Learning job openings:

Infographic showing various Research Assistant Deep Learning job openings in Prairie View, TX as of June 2026, with employment types broken down into 1% As Needed, 74% Full Time, 19% Part Time, 3% Temporary, and 3% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $41,098 per year, or $19.8 per hour.

Senior Algorithm Engineer, Reinforcement Learning

Bot Auto

Houston, TX

$99K - $137K/yr

Full-time

Medical, PTO

Re-posted 8 days ago


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 startup and the wisdom of seasoned experts, our team has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create groundbreaking solutions that propel the future of transportation. Join us and transform your ideas into reality.

Role Overview

We are seeking a Senior ML/RL Engineer to join our Algo team and drive the development of our unified behavioral architecture. In this role, you will help bridge the gap between simulation and the real world by developing a scalable policy framework that represents both our L4 ego-policy and a diverse population of simulated agents. You will work at the intersection of Multi-Agent Reinforcement Learning (MARL) and safety-critical system design to ensure our autonomous semi-trucks navigate highways with superhuman safety and precision.

Key Responsibilities
  • Behavioral Modeling: Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress-test and validate our autonomous driving stack.
  • Safety-Constrained Learning: Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated as primary constraints in the learning process.
  • Reward & Objective Design: Collaborate with cross-functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
  • Scalable Training Pipelines: Contribute to the optimization of our large-scale, high-throughput training environments to enable rapid iteration on complex multi-agent scenarios.
  • Model Architecture: Advance our state-of-the-art neural architectures to improve spatial reasoning, long-horizon planning, and interaction modeling.
  • Cross-Team Collaboration: Work closely with Simulation and Planning teams to integrate research-grade models into production-quality, safety-critical software.
Required Qualifications
  • Professional RL Experience: Proven track record of training and deploying deep RL algorithms (e.g., PPO, SAC) for complex, real-world robotic or autonomous systems.
  • Technical Mastery: Expertise in Python and PyTorch; strong understanding of modern deep learning architectures and optimization techniques.
  • Academic Background: MS or PhD in Computer Science, Robotics, or a related quantitative field.
  • Scientific Intuition: Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
Preferred Qualifications
  • Safe RL Specialization: Experience with constrained optimization or safety-critical learning frameworks.
  • Multi-Agent Systems: Background in MARL training stability, including self-play and decentralized execution strategies.
  • Autonomous Driving Domain: Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.
Additional Information
  • Compensation: Competitive salary based on experience, with opportunities for performance bonuses and equity.
  • Benefits: Comprehensive health insurance, paid time off, and the opportunity to work at the forefront of the autonomous trucking industry.
Why Bot Auto?

We are a small, hyper-focused team on a mission to beat human cost-per-mile through technology. We recently successfully completed the industry's first fully humanless commercial truckload, proving that our vision is a reality. If you are passionate about AI, safety, and transforming logistics, we want to hear from you.