---
title: "Autonomous Learning Engineer at Bright Vision Technologies – All Job Board"
---
# Autonomous Learning Engineer at Bright Vision Technologies
Written by
[admin](https://alljobboard.com/author/admin/)
in
- [United States (Remote)](https://maps.google.com/maps?q=United%20States%20%28Remote%29&zoom=14&size=512x512&maptype=roadmap&sensor=false)
- Posted 3 weeks ago
**Bright Vision Technologies**
**Bright Vision Technologies** is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
### Job Title
### Autonomous Learning Engineer
**Location:** 100% Remote (U.S.)
**Position Type:** Full-time, Direct W2
**Salary Range:** $130,000–$180,000 Annually
**Experience Required:10+ Years**
**Sponsorship:** U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
### Job Summary
Bright Vision Technologies is seeking a highly experienced **Autonomous Learning Engineer** with **10+ years of experience** in Artificial Intelligence, Reinforcement Learning (RL), and Deep Learning to design, train, and deploy intelligent decision-making systems for complex real-world applications. The ideal candidate will possess deep expertise in **Python, reinforcement learning, deep learning, simulation environments, distributed training, and RLHF (Reinforcement Learning from Human Feedback)** while driving the architecture and deployment of scalable, production-ready autonomous learning solutions.
### Key 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 RL algorithms, reward models, and policy optimization techniques.
- Build autonomous learning systems leveraging **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 while 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.
### Required Qualifications
- 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.
### Preferred Qualifications
- 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.
**Interested in this opportunity? Apply today for immediate consideration!**
Email your updated resume:
### Call or Text: (908) 505-3545
Learn more:
**Bright Vision Technologies is an Equal Opportunity Employer.**
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees’ ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Originally posted on Himalayas
**Source:** this listing was published by [Himalayas](https://himalayas.app/). [View the original posting and apply](https://himalayas.app/companies/bright-vision-technologies/jobs/autonomous-learning-engineer-5012891952?utm_source=api) — applications are handled there, not on this site.
To apply for this job please visit [himalayas.app](https://himalayas.app/companies/bright-vision-technologies/jobs/autonomous-learning-engineer-5012891952?utm_source=api).
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"title": "Autonomous Learning Engineer at Bright Vision Technologies",
"description": " Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
\nJob Title
\nAutonomous Learning Engineer
\nLocation: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $130,000–$180,000 Annually
Experience Required:10+ Years
\nSponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
\nJob Summary
\n Bright Vision Technologies is seeking a highly experienced Autonomous Learning Engineer with 10+ years of experience in Artificial Intelligence, Reinforcement Learning (RL), and Deep Learning to design, train, and deploy intelligent decision-making systems for complex real-world applications. The ideal candidate will possess deep expertise in Python, reinforcement learning, deep learning, simulation environments, distributed training, and RLHF (Reinforcement Learning from Human Feedback) while driving the architecture and deployment of scalable, production-ready autonomous learning solutions.
\nKey Responsibilities
\n\n- Design, develop, and deploy advanced reinforcement learning solutions for complex decision-making and autonomous systems.
\n- Architect scalable reinforcement learning training pipelines using distributed computing and GPU-accelerated infrastructure.
\n- Design, build, and optimize simulation environments for training and validating reinforcement learning agents.
\n- Develop, implement, and evaluate modern RL algorithms, reward models, and policy optimization techniques.
\n- Build autonomous learning systems leveraging RLHF, imitation learning, offline reinforcement learning, and multi-agent learning approaches.
\n- Improve model convergence, sample efficiency, training stability, inference performance, and production scalability.
\n- Integrate reinforcement learning models into production applications while ensuring reliability, safety, monitoring, and continuous improvement.
\n- Collaborate with AI researchers, data scientists, software engineers, and product teams to deliver enterprise-scale AI solutions.
\n- Mentor engineers and provide technical leadership on reinforcement learning architecture, experimentation, and engineering best practices.
\n- Evaluate emerging reinforcement learning frameworks, algorithms, and research to drive continuous innovation.
\n
\nRequired Qualifications
\n\n- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Mathematics, or a related technical discipline.
\n- 10+ years of professional experience in Artificial Intelligence, Machine Learning, Deep Learning, or Reinforcement Learning.
\n- Expert-level programming skills in Python and extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
\n- Strong experience with reinforcement learning libraries such as Ray RLlib, Stable-Baselines3, CleanRL, or Acme.
\n- Hands-on experience developing simulation environments using tools such as Gymnasium/OpenAI Gym, Isaac Sim, MuJoCo, Unity ML-Agents, or NVIDIA Omniverse.
\n- Experience with distributed training, GPU acceleration, model optimization, and large-scale AI infrastructure.
\n- Strong understanding of reinforcement learning theory, optimization, probability, stochastic processes, and decision-making algorithms.
\n- Experience deploying AI solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
\n- Excellent analytical, communication, collaboration, and technical leadership skills.
\n
\nPreferred Qualifications
\n\n- Experience with RLHF, multi-agent reinforcement learning, robotics, autonomous systems, or control systems.
\n- Hands-on experience with foundation models, LLM alignment, agentic AI, or autonomous AI agents.
\n- Experience with MLOps, Kubernetes, Docker, CI/CD pipelines, and production AI deployment.
\n- Publications in top AI conferences, open-source contributions, patents, or recognized technical leadership in reinforcement learning.
\n- Knowledge of Responsible AI, AI safety, model governance, explainability, and regulatory compliance.
\n- Ph.D. or Master’s degree specializing in Artificial Intelligence, Machine Learning, Reinforcement Learning, Robotics, or Control Systems.
\n
\nInterested in this opportunity? Apply today for immediate consideration!
\nEmail your updated resume:
\nCall or Text: (908) 505-3545
\nLearn more:
\n Bright Vision Technologies is an Equal Opportunity Employer.
\nEqual Employment Opportunity (EEO) Statement
\n Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
\nBV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees’ ability to perform their job duties may result in disciplinary action up to and including termination of employment.
\nOriginally posted on Himalayas
\nSource: this listing was published by Himalayas. View the original posting and apply — applications are handled there, not on this site.
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