Hiring: Benjamin RL
We’re training the next generation of Vedika models.
This role sits directly inside RL and post-training research: designing how the model learns after pretraining, how it improves from interaction, how it handles long-horizon tasks, and how we push capability beyond standard instruction tuning.
You’ll work on:
• RL training for next-generation Vedika models
• GRPO, PPO, DPO and newer post-training methods
• Reward models, process rewards and verifiers
• Long-horizon reasoning and agent trajectories
• Tool-use and computer-use reinforcement
• Self-improvement and synthetic training loops
• Multi-turn behaviour and memory training
• Failure mining from model trajectories
• Evaluation systems for reasoning, autonomy and reliability
• Research experiments that can become part of the next model generation
Compensation:
₹2.6 LPA fixed
₹3.6 LPA CTC
Work mode: Fully remote
You’ll get:
• Mac for development
• Claude
• Codex
• Serious compute and research infrastructure
• ₹10L–₹50L+ yearly AI/token spend available across the team and experiments
This is not a role for someone whose idea of model work ends at prompting or basic fine-tuning.
We want someone who can understand a training run, break it, diagnose it, redesign it and make the next model measurably better.
Strong PyTorch, RL fundamentals, post-training, distributed training and hands-on experimentation matter far more than credentials.
Role: Benjamin RL
Vedika — Next Generation Models
Send your work, experiments, papers, repos or anything you trained that genuinely got better.