AI Learning Systems Engineer

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 AI Learning Systems 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.

Location: 100% Remote (Continental United States)
Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor)
Experience: 6+ years
Salary: $100K- $150K

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.
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.

We are looking for an AI Learning Systems 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  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.

• 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…