Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The AI Learning Systems Engineer will design, train, and deploy RL-based systems for high-impact decision-making problems, requiring expertise in modern reinforcement learning algorithms and engineering practices.
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
Skills
- 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
Company Overview