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Helper Reinforcement Learning Jobs in Texas (NOW HIRING)

Apptronik

Software Engineer - Human Motion Data

Austin, TX · On-site

$113K - $136K/yr

Collaborate closely with the Reinforcement Learning and Controls teams to iterate on data ... Proficiency in C++ is highly valued to help integrate with our broader robotics software stack.

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

This role will assist our Online Retail Decision Automation team by helping to research and develop ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Roku

Senior Machine Learning Engineer

Austin, TX

$121K - $160K/yr

We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction ... Help build a first-class machine learning platform from the ground up which manages the entire ...

Apple

SoC Machine Learning Design Engineer

Austin, TX · On-site

Come help us design the next generation of revolutionary Apple products. We are looking for a ... Experience with Deep neural networks and reinforcement learning is a plus Solid math background and ...

Apple Inc.

SoC Machine Learning Design Engineer

Austin, TX · On-site

$120 - $160/hr

Come help us design the next generation of revolutionary Apple products. We are looking for a ... Experience with Deep neural networks and reinforcement learning is a plus * Solid math background ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

This role will assist our Online Retail Decision Automation team by helping to research and develop ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

This role will assist our Online Retail Decision Automation team by helping to research and develop ... Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning ...

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Helper Reinforcement Learning information

What is the difference between Helper Reinforcement Learning vs Data Scientist?

AspectHelper Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of reinforcement learningDegree in Data Science, Statistics, Computer Science; proficiency in programming and analytics
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, consulting firms, tech companies
Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights
Common Search/ComparisonHelper Reinforcement Learning vs Data Scientist

Helper Reinforcement Learning focuses on developing algorithms that enable machines to learn through interactions, often requiring knowledge of reinforcement learning techniques. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve programming and data handling, Helper Reinforcement Learning is more specialized in AI algorithm development, whereas Data Scientists work broadly across data analysis and modeling in various industries.

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Helper Reinforcement Learning jobs near you

Algorithm Engineer, Reinforcement Learning

Bot Auto

Houston, TX • On-site

$56 - $77/hr

Full-time

Medical, PTO

Re-posted 14 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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