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Open nowPosted 6 hours ago

Founding Data Scientist - Gurugram

CLANX29 open roles

Where
Gurgaon (Gurugram), Haryāna, India
Work mode
On site
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Your applicationOpen nowFounding Data Scientist - GurugramCLANX · Gurgaon (Gurugram), Haryāna, India

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The clock on this job

Early applications get read.

13.9% of postings close within 7 days. Measured by our own scanner across the market. CLANX postings stay open a median of 15 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.9%7 days
  4. 22.4%14 days
  5. 38.1%30 days
  6. 59.0%60 days
This job: posted 6 hours ago

CLANX median: 15 days open

The posting

Join Valo as a Founding Data Scientist and build models that learn from real-world user interactions. Own the training, evaluation, and reinforcement learning systems powering AI personal assistants for millions of Indians.

Company Details

Valo (by Persin) is building an AI-powered personal assistant that every Indian can afford. The vision is to make personal assistance accessible to millions of working adults across India, helping them manage everyday tasks through phone calls, WhatsApp, payments, and digital services.

Valo is backed by three VCs and led by an experienced founding team with backgrounds at Zomato, Magicpin, IDFC Bank, Grab, Microsoft, Airtel, and Kapture CX.

Website: https://valo.persin.ai/

Requirements

  • 3+ years of professional 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

Important Note

ClanX is a recruitment partner, helping Valo hire a Founding Data Scientist.

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