Research Intern — Applied Reinforcement Learning

Centific

1d•Remote

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 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.

Requirements

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)

Nice To Haves

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

Responsibilities

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
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