Requirements
• 3+ years of qualified experience in Data Science, Machine Learning, or Applied AI.
• Currently working at a product-based company.
• Previous experience being part of a founding or early-stage engineering or data science team, preferably having taken a product from 0 to 1.
• Hands-on experience post-training open-weight LLMs such as Llama, Qwen, Gemma, or similar models.
• Strong experience with SFT, DPO, and other reinforcement learning techniques, with evidence of taking trained models to production.
• Experience building datasets for AI agents, including collecting, filtering, labelling, and deduplicating trajectory and preference data.
• Experience designing evaluation datasets and frameworks to measure model performance.
• Hands-on experience with PyTorch, distributed training using FSDP or DeepSpeed, mixed precision, and LoRA/PEFT.
• Experience with high-throughput inference and rollouts using vLLM or SGLang.
• Deep understanding of LLMs and transformer architecture, including attention, tokenisation, pre-training, and instruction tuning.
• High ownership, high agency, and the ability to work independently in an early-stage startup environment.
• Open to working in-office in Gurgaon six days a week.
• Genuinely interested in joining a startup and building products from zero to one. Responsibilities




• Build and improve models that learn from real-world consumer interactions and task outcomes.
• Own the post-training pipeline for open-weight LLMs using SFT, DPO, and reinforcement learning techniques.
• Design training systems that use agent trajectories, user preferences, and task outcomes as training signals.
• Build and maintain datasets by collecting, filtering, labelling, and deduplicating trajectory and preference data.
• Define what constitutes a successful task outcome and translate it into measurable training rewards.
• Build evaluation frameworks to measure model performance and decide when internally trained models can replace external frontier models.
• Run high-throughput rollouts and inference using vLLM or SGLang.
• Own distributed training workflows using PyTorch, FSDP, DeepSpeed, and multi-GPU infrastructure.
• Work closely with the founders and engineering team to improve model intelligence, reliability, and decision-making.
• Establish the model training and evaluation foundation as part of Valo's founding team. Job Details Gurgaon (In-office, 6 days a week) Interview Process
• Technical Screening
• ML/LLM Engineering Round
• Model Training and Research Round
• Founders' Round
• Culture Fit Round

📌 Data Scientist (Gurugram)
🏢 ClanX
📍 Gurugram