AI ENGINEER, REINFORCEMENT LEARNING

Oureon Technologies, Inc.; Austin, Northern (TX, KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

401(k) plan

Equity stock options

Free daily lunch and snacks

Job summary

Oureon Technologies, Inc. in Austin, TX is seeking an AI Engineer specializing in reinforcement learning to architect and deploy the intelligence layer for our autonomy software.

You will design, train, and integrate RL-based decision systems that operate in real-time across dynamic environments. The role emphasizes production-grade Python and Rust, simulation-to-real transfer, and collaboration with autonomy and systems engineers to ensure safe, scalable deployment.

Qualifications

• 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, actor-critic, Q-learning, PPO, SAC).
• Strong grasp of simulation environments, reward shaping, and training stability techniques.

Responsibilities

• Design and implement reinforcement learning frameworks for autonomy decision-making and control.
• Develop and optimize policy learning, reward modeling, and environment simulation for multi-agent systems.
• Build training pipelines that scale across simulation and real-world telemetry data.
• Collaborate with autonomy and systems engineers to deploy models into live control layers.
• Implement safe exploration, policy evaluation, and deployment validation mechanisms.
• Profile and optimize learning performance across distributed compute and cloud environments.
• Translate models into real-time inference modules within the autonomy stack.
• Participate in architecture reviews to align RL with autonomy goals.

Skills

Reinforcement learning

Control optimization

Sequential decision-making

Python

Rust

PyTorch

TensorFlow

Distributed training

GPU acceleration

Mathematical optimization

Tools

PyTorch

TensorFlow

Rust tooling

Custom Rust inference modules

Job description

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