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Algorithmic Execution Quant Jobs in Sugar Land, TX

Bot Auto

Algorithm Engineer, Reinforcement Learning

Houston, TX · On-site

$56 - $77/hr

  • Medical

  • PTO

MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition ... Background in MARL training stability, including self-play and decentralized execution strategies.

Bot Auto

Senior ML/RL Engineer, Behavior Planning

Houston, TX · On-site

$99K - $137K/yr

Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated ... a related quantitative field. • Ability to diagnose and solve fundamental challenges in RL ...

All JobsAlgorithmic Execution Quant JobsAlgorithmic Execution Quant Jobs in Sugar Land, TX

Algorithmic Execution Quant information

See Sugar Land, TX salary details

$47.1K

$106.8K

$176.2K

How much do algorithmic execution quant jobs pay per year?

As of Aug 15, 2026, the average yearly pay for algorithmic execution quant in Sugar Land, TX is $106,837.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,400.00 and $136,700.00 per year, depending on experience, location, and employer.

What is the difference between Algorithmic Execution Quant vs Quantitative Trader?

AspectAlgorithmic Execution QuantQuantitative Trader
Primary FocusDeveloping and implementing algorithms for trade execution to minimize market impactCreating trading strategies to generate alpha and profit from market movements
Work EnvironmentQuantitative research teams, trading desks, technology-drivenTrading floors, portfolio management teams, research departments
Required SkillsProgramming, market microstructure, execution algorithmsQuantitative modeling, market analysis, strategy development

While both roles involve quantitative skills, an Algorithmic Execution Quant specializes in optimizing trade execution processes, whereas a Quantitative Trader focuses on developing strategies to generate profits. The roles often collaborate but serve different functions within trading firms.

What are the key skills and qualifications needed to thrive as an algorithmic execution quant, and why are they important?

To thrive as an Algorithmic Execution Quant, you need a strong background in quantitative analysis, programming (often in Python or C++), and a solid understanding of financial markets, typically supported by an advanced degree in a quantitative discipline. Proficiency with statistical modeling tools, trading platforms, and market data systems, as well as familiarity with technologies like FIX protocol, is crucial. Strong problem-solving ability, attention to detail, and effective communication help you collaborate across trading, research, and technology teams. These skills are essential for designing, optimizing, and maintaining robust trading algorithms that achieve best execution and mitigate risk in fast-moving markets.

What are some common challenges faced by algorithmic execution quants when developing and deploying trading algorithms?

Algorithmic Execution Quants often encounter challenges such as adapting strategies to rapidly changing market conditions, managing latency and slippage, and ensuring compliance with regulatory requirements. They must also balance the need for innovation with the necessity for robust risk controls and system reliability. Collaboration with traders, developers, and risk managers is essential to refine algorithms and ensure they perform optimally in live trading environments.

What does an algorithmic execution quant do?

An Algorithmic Execution Quant is responsible for designing, developing, and optimizing algorithms that execute large financial trades efficiently and at minimal cost. They analyze market microstructure, create models to predict market impact, and work closely with traders and engineers to implement these strategies in real-time trading systems. Their work is essential in minimizing transaction costs and improving trade execution quality for their firm.

What are popular job titles related to Algorithmic Execution Quant jobs in Sugar Land, TX?

For Algorithmic Execution Quant jobs in Sugar Land, TX, the most frequently searched job titles are:

What job categories do people searching Algorithmic Execution Quant jobs in Sugar Land, TX look for?

The top searched job categories for Algorithmic Execution Quant jobs in Sugar Land, TX are:

What cities near Sugar Land, TX are hiring for Algorithmic Execution Quant jobs?

Cities near Sugar Land, TX with the most Algorithmic Execution Quant job openings:

Algorithmic Execution Quant jobs near you
Infographic showing various Algorithmic Execution Quant job openings in Sugar Land, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $106,837 per year, or $51.4 per hour.

Algorithm Engineer, Reinforcement Learning

Bot Auto

Houston, TX • On-site

$56 - $77/hr

Full-time

Medical, PTO

Re-posted 21 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 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.

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