Applied Reinforcement Learning Engineer

Centific•Redmond, WA

1d•$150,000 - $160,000•Hybrid

About The Position

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.

Requirements

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)

Nice To Haves

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

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

Benefits

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

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