We're building a governed RL environment platform that enables enterprises to safely iterate and improve AI agent workflows through simulation-based learning, bridging human-labeled signal creation with automated RL training for high-stakes operations.
Role Overview
As an Applied RL Engineer, you will design and build RL environments that simulate complex enterprise workflows and train intelligent agents within them. You'll work at the intersection of RL research and production systems, translating customer requirements into bespoke simulation environments and post-training pipelines that deliver measurable improvements to AI agent performance.
This role requires deep expertise in both classical RL methodologies and modern LLM-based agent architectures. You'll shape our product direction and help make RL accessible to enterprise customers who need safe, compliant ways to improve their AI systems.
Core RL Competencies
Foundational RL
MDPs & value methods: State/action spaces, Q-learning, DQN, Double DQN, Dueling DQN
Policy gradient methods: REINFORCE, Actor-Critic, A2C/A3C, variance reduction
Advanced optimization: PPO, TRPO, SAC, trust regions, entropy regularization
TD learning: TD(0), TD(λ), eligibility traces, bootstrapping methods
LLM Alignment & Post-Training
RLHF pipelines: Reward model training, preference learning, human feedback integration
Direct optimization: DPO, IPO, KTO, offline preference optimization
Group-based methods: GRPO, RLOO, sample-efficient policy improvement
Reward modeling: Bradley-Terry models, reward hacking mitigation, KL constraints
Environment Design
Gymnasium/OpenAI Gym: Custom environments, observation/action spaces, wrapper patterns
Reward engineering: Sparse vs. dense rewards, potential-based shaping, intrinsic motivation
Verifier design: Programmatic reward functions, outcome verification, ground-truth evaluation
Simulation: Sim-to-real transfer, domain randomization, multi-agent dynamics
Advanced Techniques
Offline RL: CQL, BCQ, IQL for learning from fixed datasets without environment interaction
Model-based RL: World models, Dreamer, MuZero, learned dynamics
Hierarchical RL: Options framework, goal-conditioned policies, temporal abstraction
Imitation & exploration: Behavioral cloning, GAIL, curiosity-driven exploration, UCB
Key Responsibilities
Design and build custom RL environments (digital twins) 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
Required Qualifications
Deep RL expertise: 3+ years hands-on experience with environment design, reward engineering, policy optimization
LLM post-training: Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar
Production skills: Software engineering beyond research with scalable pipelines and training infrastructure
Agentic AI: Experience with LLM-based agents, tool use, multi-step reasoning
Technical stack: Strong Python; Gymnasium, RLlib, Stable Baselines; PyTorch/JAX/TensorFlow
Education: MS/PhD in CS, ML, or related field (or equivalent experience)
Preferred Qualifications
Publications at NeurIPS, ICML, ICLR, ACL, or similar venues
Enterprise workflow experience in healthcare, finance, logistics, or compliance
Open-source contributions to CleanRL, TRL, veRL, or agent frameworks
Experience with world models, synthetic data generation, and simulation
Distributed training and large-scale RL experimentation
Why Join Centific
Lead the frontier: Shape a new discipline at the intersection of RL, simulation, and enterprise AI
Ship your science: See your research power real systems across healthcare, finance, and safety
Collaborate with leaders: Work alongside NVIDIA, Microsoft, and the global AI community
Build what matters: Create governed, compliant AI systems enterprises can trust.
Salary: $150K - $300K Annually
Centific is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.
