Applied Reinforcement Learning Engineer
Centific Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at Centific. Remote, United States · full-time · $150,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build simulated enterprise workflows for document handling, compliance, onboarding and support automation
- Train LLM agents on specialized tasks with methods such as PPO, GRPO, DPO and RLHF
- Turn human-labeled interaction traces into data for reinforcement learning pipelines
- Create agents that reason across steps, use tools and improve through repeated learning cycles
- Develop reward rules and verification checks to test agent performance before deployment
What you bring
- At least three years designing RL environments, engineering rewards and optimizing policies in hands-on work
- Experience fine-tuning language models with RLHF, DPO, PPO or a comparable approach
- Strong Python skills plus experience with Gymnasium, RLlib, Stable Baselines and a listed ML framework
- Production software engineering experience building scalable training systems and pipelines
- A master's or doctorate in computer science, machine learning or a related area, or equivalent experience
Who this fits
This role suits an engineer who can move between reinforcement learning research and production software, and who has worked with tool-using agents and multi-step reasoning. The position is remote in the United States, with Palo Alto or Seattle also listed as location options.
From the employer
About Centific
Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.
Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.
About Job
Role: Applied Reinforcement Learning Engineer
Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote)
About the Team
Centific AI Research advances foundational AI models and applications through reinforcement learning, alignment, and human-centered intelligence. Our mission is to transform data, signals, and human insight into next-generation intelligent systems that redefine enterprise intelligence.
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