Applied RL Engineer — Enterprise AI Agents (Hybrid/Remote)

Centific; United States; Hybrid; USD 150,000 - 300,000; Full time

Job summary

Centific is seeking an Applied Reinforcement Learning Engineer to design RL environments that simulate complex enterprise workflows and train intelligent agents within them. You will bridge RL research and production, translating customer requirements into bespoke simulation pipelines that deliver measurable improvements in AI agent performance.

This role requires deep RL expertise, experience with LLM-based agents, RLHF and scalable training infrastructure.

Qualifications

• 3+ years hands-on experience with environment design and reward engineering.
• Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar.
• Production-grade software engineering with scalable pipelines and training infrastructure.
• Experience with LLM-based agents, tool use, and multi-step reasoning.
• MS/PhD in CS/ML or related field (or equivalent experience).

Responsibilities

• Design and build custom RL environments simulating enterprise workflows (document processing, compliance, onboarding, support automation).
• Post-train LLM-based agents on domain-specific tasks using PPO, GRPO, DPO, and RLHF.
• Build end-to-end pipelines converting human-labeled traces into RL training data.
• Architect multi-step reasoning agents with tool-calling and closed learning loops.
• Design reward functions, verifiers, and validation frameworks for pre-deployment testing.
• Translate cutting-edge RL research into production systems; contribute to publications.

Skills

Deep RL

RL Environment Design

LLM Alignment

Python

Production Systems

Multi-agent RL

Education

MS/PhD in CS/ML or related field

Tools

Gymnasium

RLlib

Stable Baselines

PyTorch

JAX

TensorFlow

Job description

Centific is seeking an Applied Reinforcement Learning Engineer to design RL environments that simulate complex enterprise workflows and train intelligent agents within them. You will bridge RL research and production, translating customer requirements into bespoke simulation pipelines that deliver measurable improvements in AI agent performance.

This role requires deep RL expertise, experience with LLM-based agents, RLHF and scalable training infrastructure.