Reinforcement Learning Engineer

Reinforcement Learning Engineer

We are seeking a talented Reinforcement Learning (RL) Engineer to design, develop, and optimize intelligent agents capable of learning through interaction with complex environments. The ideal candidate will have strong expertise in machine learning, deep learning, reinforcement learning algorithms, and software engineering. You will collaborate with cross-functional teams to build scalable AI solutions for real-world applications.

• Design, implement, and evaluate reinforcement learning algorithms for complex decision-making problems.

• Develop and optimize RL models using state-of-the-art techniques such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), Advantage Actor-Critic (A2C/A3C), and Deep Deterministic Policy Gradient (DDPG).

• Build custom simulation environments and integrate with frameworks such as Gymnasium, Isaac Gym, or Unity ML-Agents.

• Train, validate, and fine-tune RL agents for performance, stability, and scalability.

• Work with large-scale datasets and distributed training infrastructure.

• Collaborate with data scientists, ML engineers, software developers, and product teams to deploy RL solutions into production.

• Monitor model performance, conduct experiments, and improve learning efficiency.

• Stay current with the latest research in reinforcement learning and AI.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Mathematics, or a related field.

• 3 years of experience in machine learning or AI development.

• Strong understanding of reinforcement learning concepts, including:

  • Markov Decision Processes (MDPs)

  • Value-based methods

  • Policy Gradient methods

  • Actor-Critic algorithms

  • Model-based and model-free RL

  • Exploration vs. exploitation strategies

• Proficiency in Python.

• Experience with deep learning frameworks such as PyTorch or TensorFlow.

• Hands-on experience with RL libraries such as Stable-Baselines3, RLlib, CleanRL, or Tianshou.

• Strong knowledge of probability, statistics, linear algebra, and optimization.

• Experience with Git, Docker, Linux, and cloud platforms (AWS, Azure, or Google Cloud).

• Familiarity with REST APIs and software engineering best practices.

• Experience with Large Language Models (LLMs), Agentic AI, or Multi-Agent Reinforcement Learning (MARL).

• Experience in robotics, autonomous systems, recommendation systems, gaming, finance, or operations research.

• Knowledge of distributed training frameworks such as Ray.

• Experience with CUDA or GPU optimization.

• Publications or open-source contributions in reinforcement learning or AI.

• Python

• PyTorch / TensorFlow

• Stable-Baselines3

• RLlib

• Gymnasium

• Isaac Gym

• Unity ML-Agents

• NumPy

• Pandas

• Docker

• Kubernetes (preferred)

• Git

• SQL

• Linux

• AWS / Azure / Google Cloud

• Strong analytical and problem-solving abilities.

• Excellent communication and collaboration skills.

• Ability to work independently and in cross-functional teams.

• Strong research mindset with attention to detail.

• Passion for continuous learning and innovation.

• Experience with Generative AI and LLM fine-tuning.

• Knowledge of MLOps and CI/CD pipelines.

• Experience with reinforcement learning from human feedback (RLHF).

• Familiarity with vector databases and AI agent frameworks.

• Competitive salary and performance bonuses.

• Flexible work arrangements.

• Health and wellness benefits.

• Learning and certification opportunities.

• Access to high-performance GPU infrastructure.

• Collaborative and innovative work environment.