Research Intern — Applied Reinforcement Learning

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 PhD Research Intern — Applied Reinforcement Learning Centific AI Research Role Summary Centific AI Research seeks a PhD Research Intern to design and evaluate reinforcement learning (RL) systems for agentic AI workflows. You will develop RL environments, reward models, and post-training pipelines for LLM-based agents, translating research into practical enterprise solutions. Scope of Work - End-to-end RL pipelines for agentic systems (simulation → training → evaluation) - Alignment of LLM-based agents using RLHF, DPO, PPO, and emerging methods - Design of reward functions, verifiers, and evaluation frameworks - Simulation environments (digital twins) for enterprise workflows - Scalable training and inference for RL-based systems Example Projects - Build a custom RL environment simulating a real-world enterprise workflow and train an agent using PPO or GRPO - Develop a reward modeling pipeline from human feedback and evaluate alignment improvements - Create an evaluation harness measuring reasoning, task success, and policy safety - Prototype an agentic system with tool use and multi-step reasoning, integrated with RL training - Document experiments, ablations, and findings for research and productionization Minimum Qualifications - PhD candidate in CS, ML, or related field with research in reinforcement learning or agentic AI - Strong Python and PyTorch skills with GPU-based training experience - Solid understanding of RL fundamentals (MDPs, policy gradients, value methods) - Experience with LLMs and post-training techniques (RLHF, DPO, PPO, etc.) - Strong experimentation practices (ablation, reproducibility, clear reporting) Preferred Qualifications - Experience with RL environments (Gymnasium, RLlib , Stable Baselines) - Research in offline RL, model-based RL, or hierarchical RL - Publications at top ML conferences ( NeurIPS , ICML, ICLR, ACL) - Experience with simulation, synthetic data, or multi-agent systems - Distributed training and large-scale experimentation Tech Stack - PyTorch, CUDA; RL libraries (Gymnasium, RLlib , Stable Baselines) - LLM frameworks and post-training tools (TRL, custom RLHF pipelines) - Experiment tracking (Weights & Biases) - APIs/services ( FastAPI , gRPC); optimization (ONNX, TensorRT) Logistics Location: Palo Alto, CA (Preferred) , Redmond , WA (Preferred ) or Remote Duration: 3–6 months What We Offer - Competitive stipend and real-world impactful projects - Mentorship from researchers and engineers - Access to modern GPU infrastructure - Opportunities to publish and present research Centific AI Research is an Equal Opportunity Employer. We celebrate diversity and are committed to an inclusive environment. Rate: $35-$45 Hourly 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.