About Dusker AI
Dusker AI specializes in benchmarking and evaluating AI agents to help organizations understand real-world performance. Using expert-driven frameworks, we assess AI systems across reasoning, reliability, adaptability, and safety to ensure they are truly production-ready. From conversational AI to autonomous agentic systems, we build cutting-edge evaluation frameworks that enable organizations to develop trustworthy, high-performing AI solutions.

Role Overview
We are seeking an experienced RLHF / Post-Training Engineer to build the preference data pipelines, reward models, and alignment training loops that turn raw model capability into dependable behaviour. You will own supervised fine-tuning and preference optimization workflows end to end, from response sampling and reward modeling through training runs and post-training evaluation. You will work alongside evaluation scientists, domain experts, and infrastructure engineers to close the loop between measured model weaknesses and the training interventions that actually fix them.



This role is ideal for someone passionate about preference learning, reward modeling, alignment methodology, and rigorous measurement of post-training gains.

Key Responsibilities
• Design and run supervised fine-tuning and preference optimization pipelines using methods such as PPO, DPO, and GRPO.
• Build reward models and rubric-based graders that convert expert human judgment into reliable training signal.
• Architect preference data collection workflows covering response sampling, pairwise comparison design, and adjudication of disagreement.
• Develop reinforcement learning loops with verifiable rewards for reasoning, tool calling, and instruction following.
• Conduct systematic ablations to isolate which data mixtures, objectives, and hyperparameters genuinely drive gains.
• Collaborate with evaluation scientists to measure post-training effects on reasoning, safety, refusal behaviour, and regression risk.
• Optimize d

📌 Training Engineer (India)
🏢 Dusker AI
📍 India