O
AI ENGINEER, REINFORCEMENT LEARNING
Oureon Technologies, Inc.
2 hours ago
Full-time
On-site
Austin, Texas, United States
We're seeking an AI Engineer specializing in Reinforcement Learning (RL) to architect and implement the intelligence layer driving Oureon's autonomy software. You'll design, train, and deploy RL-based systems that enable adaptive, real-time decision-making and control in dynamic environments.
This role requires a deep understanding of RL algorithms, control optimization, and real-world deployment constraints, with strong engineering discipline in Python and Rust.
What You'll Be Doing
Design and implement reinforcement learning frameworks for autonomy decision-making and control
Develop and optimize policy learning, reward modeling, and environment simulation for complex, multi-agent systems
Build training pipelines that scale across simulation and real-world telemetry data
Collaborate with autonomy and systems engineers to integrate trained models into live autonomy control layers
Implement safe exploration, policy evaluation, and deployment validation mechanisms
Profile and optimize learning performance across distributed compute and cloud environments
Translate theoretical models into deployable, real-time inference modules within Oureon's autonomy stack
Participate in architecture and design reviews to ensure RL integration aligns with autonomy, data, and platform goals
Technical Requirements Required
Deep understanding of reinforcement learning, control optimization, and sequential decision-making
Proficiency in Python for experimentation and Rust for production deployment
Experience building and tuning RL algorithms (policy gradient methods, actor-critic, Q-learning, PPO, SAC, etc.)
Strong grasp of simulation environments, reward shaping, and training stability techniques
Familiarity with telemetry data pipelines and real-time inference systems
Understanding of distributed training, GPU acceleration, and model deployment frameworks
Strong foundation in mathematical optimization, probability, and control theory
Preferred
Experience applying RL to autonomy, robotics, or real-time control systems
Familiarity with multi-agent coordination, curriculum learning, or model-based RL
Hands‑on experience with simulation-to-real transfer and domain adaptation
Exposure to MPC (Model Predictive Control) or hybrid learning-control systems
Understanding of observability, system evaluation, and safe reinforcement learning practices
Our Stack Core Languages
Python, Rust
RL Frameworks
PyTorch, TensorFlow, custom Rust-based inference modules
Data Systems
Infrastructure
Containerized microservices, WebSocket-based messaging, distributed compute
Simulation & Training
GPU-accelerated environments, large-scale simulation orchestration
What We're Looking For We're after an engineer who bridges AI theory and systems reality - someone who can take reinforcement learning from experiment to deployment. This role is about building the intelligence that powers autonomy in live operational environments.
You’ll thrive in this role if you:
Think in terms of control, adaptation, and decision optimization
Can design learning systems that operate under real-world constraints
Are equally comfortable in research code and production infrastructure
Move fast, iterate intelligently, and validate rigorouslyWant to define how reinforcement learning drives autonomy at scale
Health, Dental, and Vision Insurance: 100% of premiums covered for employees, 80% for dependents
Compensation: Competitive salary with performance-based bonuses
Equity: Stock options offering real ownership in what you're building
Retirement: 401(k) plan
Insurance: Life, short-term, and long-term disability coverage
Perks: Free daily lunch and dinner, with snacks and drinks stocked in the office
#J-18808-Ljbffr
This role requires a deep understanding of RL algorithms, control optimization, and real-world deployment constraints, with strong engineering discipline in Python and Rust.
What You'll Be Doing
Design and implement reinforcement learning frameworks for autonomy decision-making and control
Develop and optimize policy learning, reward modeling, and environment simulation for complex, multi-agent systems
Build training pipelines that scale across simulation and real-world telemetry data
Collaborate with autonomy and systems engineers to integrate trained models into live autonomy control layers
Implement safe exploration, policy evaluation, and deployment validation mechanisms
Profile and optimize learning performance across distributed compute and cloud environments
Translate theoretical models into deployable, real-time inference modules within Oureon's autonomy stack
Participate in architecture and design reviews to ensure RL integration aligns with autonomy, data, and platform goals
Technical Requirements Required
Deep understanding of reinforcement learning, control optimization, and sequential decision-making
Proficiency in Python for experimentation and Rust for production deployment
Experience building and tuning RL algorithms (policy gradient methods, actor-critic, Q-learning, PPO, SAC, etc.)
Strong grasp of simulation environments, reward shaping, and training stability techniques
Familiarity with telemetry data pipelines and real-time inference systems
Understanding of distributed training, GPU acceleration, and model deployment frameworks
Strong foundation in mathematical optimization, probability, and control theory
Preferred
Experience applying RL to autonomy, robotics, or real-time control systems
Familiarity with multi-agent coordination, curriculum learning, or model-based RL
Hands‑on experience with simulation-to-real transfer and domain adaptation
Exposure to MPC (Model Predictive Control) or hybrid learning-control systems
Understanding of observability, system evaluation, and safe reinforcement learning practices
Our Stack Core Languages
Python, Rust
RL Frameworks
PyTorch, TensorFlow, custom Rust-based inference modules
Data Systems
Infrastructure
Containerized microservices, WebSocket-based messaging, distributed compute
Simulation & Training
GPU-accelerated environments, large-scale simulation orchestration
What We're Looking For We're after an engineer who bridges AI theory and systems reality - someone who can take reinforcement learning from experiment to deployment. This role is about building the intelligence that powers autonomy in live operational environments.
You’ll thrive in this role if you:
Think in terms of control, adaptation, and decision optimization
Can design learning systems that operate under real-world constraints
Are equally comfortable in research code and production infrastructure
Move fast, iterate intelligently, and validate rigorouslyWant to define how reinforcement learning drives autonomy at scale
Health, Dental, and Vision Insurance: 100% of premiums covered for employees, 80% for dependents
Compensation: Competitive salary with performance-based bonuses
Equity: Stock options offering real ownership in what you're building
Retirement: 401(k) plan
Insurance: Life, short-term, and long-term disability coverage
Perks: Free daily lunch and dinner, with snacks and drinks stocked in the office
#J-18808-Ljbffr