Reinforcement Learning Engineer

Remote — United States
Full time
Work from home

Salary: USD 100,000 - 150,000 / annual
Deadline: 26 Nov 202655 days left
Vacancy: Not specified
Category: IT & Telecommunication

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Summary

Requirements

Experience

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.

Additional Requirements

Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience.
Six or more years of combined RL research and engineering experience.
Strong proficiency in Python and modern deep learning frameworks.
Hands-on experience with at least one major RL library or in-house RL stack.
Solid understanding of probability, optimization, and the theoretical foundations of RL.
Experience designing and tuning reward functions in non-trivial environments.
Familiarity with simulation environments and large-scale experience collection.
Experience training neural network policies on GPU clusters.
Strong written and verbal communication skills.
Track record of shipping or publishing impactful RL work.
Experience with RLHF for large language models.
Familiarity with multi-agent RL or hierarchical RL.
Exposure to robotics, control systems, or autonomous driving.
Publications in RL or related research venues.
Open-source contributions to RL libraries or environments.

Responsibilities & Context

Full-time, Direct W2

Reinforcement Learning Engineer - Remote

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: Reinforcement Learning Engineer

Location: 100% Remote (U.S.)

How to Apply

Would you like to know more about this opportunity? For immediate consideration, please send your resume to or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at .

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

Key Responsibilities

Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
Develop, calibrate, and maintain simulation environments suitable for large-scale agent training.
Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
Apply offline RL and imitation learning techniques where exploration is costly or unsafe.
Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant.
Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems.
Optimize training stability and sample efficiency through algorithmic and engineering improvements.
Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight.
Collaborate with applied scientists and product teams to identify high-value RL use cases.
Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users.
Document methodology, design decisions, and operational characteristics for internal stakeholders.
Stay current with RL research and translate promising techniques into production-ready solutions.

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Reinforcement Learning Engineer

Summary

Requirements

Experience

Additional Requirements

Responsibilities & Context

Key Responsibilities

Skills & Expertise

Compensation & Other Benefits

Workplace

Employment Status

Job Location

How to Apply

Company Information

Report this Job / Company (রিপোর্ট)

Summary

Requirements

Experience

Additional Requirements

Responsibilities & Context

Key Responsibilities

Skills & Expertise

Compensation & Other Benefits

Workplace

Employment Status

Job Location

How to Apply

Company Information

Report this Job / Company (রিপোর্ট)

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