Autonomous Learning Engineer

Bright Vision Technologies•Alpharetta, GA

5d•$130,000 - $180,000•Remote

About The Position

Bright Vision Technologies is seeking a highly experienced Autonomous Learning Engineer to design, train, and deploy intelligent decision-making systems for complex real-world applications. The role focuses on artificial intelligence, reinforcement learning, and deep learning, including scalable, production-ready autonomous learning solutions. The position is 100% remote in the United States and is full-time, direct W2, with an annual salary range of $130,000–$180,000. The employer is unable to sponsor new H-1B visa petitions; U.S. citizens, Green Card holders, EAD holders, and candidates transferring an H-1B are encouraged to apply. The engineer will develop reinforcement learning algorithms and training pipelines, build simulation environments, and improve model performance and production scalability. The role also includes integrating models into production applications, collaborating across research and engineering teams, mentoring engineers, and evaluating emerging research and frameworks. Bright Vision Technologies is an equal opportunity employer and prohibits workplace harassment and discrimination.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Mathematics, or a related technical discipline.
10+ years of professional experience in Artificial Intelligence, Machine Learning, Deep Learning, or Reinforcement Learning.
Expert-level programming skills in Python and extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Strong experience with reinforcement learning libraries such as Ray RLlib, Stable-Baselines3, CleanRL, or Acme.
Hands-on experience developing simulation environments using tools such as Gymnasium/OpenAI Gym, Isaac Sim, MuJoCo, Unity ML-Agents, or NVIDIA Omniverse.
Experience with distributed training, GPU acceleration, model optimization, and large-scale AI infrastructure.
Strong understanding of reinforcement learning theory, optimization, probability, stochastic processes, and decision-making algorithms.
Experience deploying AI solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Excellent analytical, communication, collaboration, and technical leadership skills.

Nice To Haves

Experience with RLHF, multi-agent reinforcement learning, robotics, autonomous systems, or control systems.
Hands-on experience with foundation models, LLM alignment, agentic AI, or autonomous AI agents.
Experience with MLOps, Kubernetes, Docker, CI/CD pipelines, and production AI deployment.
Publications in top AI conferences, open-source contributions, patents, or recognized technical leadership in reinforcement learning.
Knowledge of Responsible AI, AI safety, model governance, explainability, and regulatory compliance.
Ph.D. or Master's degree specializing in Artificial Intelligence, Machine Learning, Reinforcement Learning, Robotics, or Control Systems.

Responsibilities

Design, develop, and deploy advanced reinforcement learning solutions for complex decision-making and autonomous systems.
Architect scalable reinforcement learning training pipelines using distributed computing and GPU-accelerated infrastructure.
Design, build, and optimize simulation environments for training and validating reinforcement learning agents.
Develop, implement, and evaluate modern reinforcement learning algorithms, reward models, and policy optimization techniques.
Build autonomous learning systems using RLHF, imitation learning, offline reinforcement learning, and multi-agent learning approaches.
Improve model convergence, sample efficiency, training stability, inference performance, and production scalability.
Integrate reinforcement learning models into production applications, ensuring reliability, safety, monitoring, and continuous improvement.
Collaborate with AI researchers, data scientists, software engineers, and product teams to deliver enterprise-scale AI solutions.
Mentor engineers and provide technical leadership on reinforcement learning architecture, experimentation, and engineering best practices.
Evaluate emerging reinforcement learning frameworks, algorithms, and research to drive continuous innovation.