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Algorithmic Execution Quant Jobs in Houston, TX (NOW HIRING)

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 ...

Bot Auto

ML/RL Engineer, Behavior Planning

Houston, TX · On-site

$78K - $105K/yr

Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated ... MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition:

Bot Auto

ML/RL Engineer, Behavior Planning

Houston, TX · On-site

$78K - $105K/yr

Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated ... MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition:

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 ... MS or PhD in Computer Science, Robotics, or a related quantitative field. * Scientific Intuition:

bp

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

You will apply machine learning science as a core discipline - developing novel algorithms and ... MSc or PhD degree or equivalent experience in a quantitative field (e.g. Computer Science ...

All JobsAlgorithmic Execution Quant JobsAlgorithmic Execution Quant Jobs in Houston, TX

Algorithmic Execution Quant information

See Houston, TX salary details

$50.1K

$113.8K

$187.7K

How much do algorithmic execution quant jobs pay per year?

As of Jul 26, 2026, the average yearly pay for algorithmic execution quant in Houston, TX is $113,800.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $145,600.00 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 Houston, TX? For Algorithmic Execution Quant jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Algorithmic Execution Quant jobs in Houston, TX look for? The top searched job categories for Algorithmic Execution Quant jobs in Houston, TX are:
What cities near Houston, TX are hiring for Algorithmic Execution Quant jobs? Cities near Houston, TX with the most Algorithmic Execution Quant job openings:
Algorithmic Execution Quant jobs near you

Senior ML/RL Engineer, Behavior Planning

Bot Auto

Houston, TX • On-site

$99K - $137K/yr

Full-time

Posted 4 hours ago

Job description

Job Summary:
Bot Auto is revolutionizing the transportation of goods with autonomous trucks, and they are seeking a Senior ML/RL Engineer to develop their unified behavioral architecture. This role involves bridging the gap between simulation and real-world applications by creating scalable policy frameworks and ensuring safety in autonomous navigation.
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.
Qualifications:
Required:
• Proven track record of training and deploying deep RL algorithms (e.g., PPO, SAC) for complex, real-world robotic or autonomous systems.
• Expertise in Python and PyTorch; strong understanding of modern deep learning architectures and optimization techniques.
• MS or PhD in Computer Science, Robotics, or a related quantitative field.
• Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
Preferred:
• Experience with constrained optimization or safety-critical learning frameworks.
• Background in MARL training stability, including self-play and decentralized execution strategies.
• Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.
Company:
Transforming American Transportation with Autonomous Trucks Founded in 2023, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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