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.

Robust 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.

📌 Benjamin RLHF (Pune)
🏢 Vedika API
📍 Pune