AI Learning Systems Engineer

Bright Vision Technologies•Highlands Ranch, CO

4d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Learning Systems Engineer to design, train, and deploy reinforcement-learning systems for high-impact decision-making problems where supervised learning alone is insufficient. The role calls for deep familiarity with modern reinforcement-learning algorithms, simulation environments, reward modeling, and the engineering challenges of training and evaluating policies at scale. The successful candidate will combine research depth with practical engineering experience, taking RL systems from research into production with a focus on stability, safety, and ongoing improvement. This is a full-time, direct W2 position with Bright Vision Technologies, working remotely from the continental United States. The position contributes to the company’s in-house Statement of Work engagement; Bright Vision Technologies is the employer, with no third-party client, vendor, or implementation partner involved. C2C, 1099, and third-party arrangements are not accepted. New H1B sponsorship is unavailable, though qualified candidates with a valid H1B visa who require a transfer may apply. A technical coding assessment is mandatory.

Requirements

Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience.
Six or more years of combined reinforcement-learning research and engineering experience.
Strong proficiency in Python and modern deep-learning frameworks.
Hands-on experience with at least one major RL library or an in-house RL stack.
Solid understanding of probability, optimization, and the theoretical foundations of reinforcement learning.
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 reinforcement-learning work.
Confidence in technical abilities and hands-on experience; a technical coding assessment is mandatory.

Nice To Haves

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

Design and implement reinforcement-learning solutions for sequential decision-making in real and simulated environments.
Develop, calibrate, and maintain simulation environments for large-scale agent training.
Implement and evaluate policy-gradient, actor-critic, off-policy, and offline reinforcement-learning methods.
Engineer reward functions and shaping strategies aligned with desired outcomes and safety constraints.
Apply offline RL and imitation-learning techniques when exploration is costly or unsafe.
Use RLHF, DPO, and related techniques to fine-tune large language models when relevant.
Build scalable distributed RL training infrastructure, including efficient experience-collection and replay systems.
Improve 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 production policies and models for drift, regression, and unintended behavior, and build alerting and dashboards to surface issues.
Document methodology, design decisions, and operational characteristics for internal stakeholders.
Stay current with RL research and translate promising techniques into production-ready solutions.