Applied RL Engineer

pofft Technology Inc.; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Applied RL Engineer to design, train, and deploy RL-based systems for high-impact decision problems. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and scalable training and evaluation of policies.

The position is 100% remote within the United States, full-time on a direct W2 basis, with a salary range of $100,000 to $150,000 per year.

Qualifications

• Master’s or PhD in CS/ML or equivalent experience.
• 6+ years of RL research and engineering experience.
• Strong 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 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.

Responsibilities

• Design, train, and deploy RL-based systems for decision-making.
• Develop simulation environments and reward modeling.
• Scale training and evaluate policies for production-ready deployment.

Skills

Python

Deep learning frameworks

RL research & engineering

RL libraries

Probability & optimization

Reward modeling

GPU training

Written and verbal communication

Shipping RL work

Education

Master’s/PhD in CS/ML

Tools

In‑house RL stack

Simulation environments tooling

Job description

Job Title:

Applied RL Engineer

Location:

100% Remote (United States)

Position Type:

Full-time, Direct W2

Salary Range:

$100,000–$150,000 Annually

Experience:

6+ 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.

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.

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.

Equal Employment Opportunity Statement:

Bright Vision Technologies is an Equal Opportunity Employer.

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.