Reinforcement Learning Engineer at Bright Vision Technologies

Edison, New Jersey, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Aug, 26

Salary

0.0

Posted On

17 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Python, Deep Learning Frameworks, Reward Modeling, Simulation Environments, Policy Gradient, Actor-Critic, Offline RL, RLHF, DPO, Distributed RL, Imitation Learning, GPU Clusters, Probability, Optimization, Neural Network Policies

Industry

Information Technology & Services

Description

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled Reinforcement Learning Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Reinforcement Learning Engineer Job Title: Reinforcement Learning Engineer Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience. Job Summary We are looking for a Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale. The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical. 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. Required Qualifications 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. Preferred Qualifications 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” 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.

Responsibilities

Design, train, and deploy reinforcement learning systems for high-impact decision-making problems. Develop simulation environments, engineer reward functions, and monitor deployed policies for stability and safety in production.