Weekday AI

Founding Data Scientist

Gurugram
Level
Senior

Not stated in the posting: salary, work type, visa.

Open. First seen 3 November 2026.

Apply on the company's site →

About the role

This role is for one of Weekday’s clients

Min Experience: 8+ years
Location: Gurgaon
JobType: full-time

We are looking for a highly skilled and hands-on Founding Data Scientist to build, fine-tune, and post-train large language models (LLMs) that power autonomous AI agents. This is a founding-level opportunity to take ownership of the complete model improvement lifecycle, from training data creation and reward design to supervised fine-tuning (SFT), reinforcement learning, evaluation, and production deployment.

The organisation is building an AI-powered personal assistant designed to help users complete real-world tasks, including making calls, managing bookings, coordinating services, and handling payments. Its autonomous AI agents are designed to plan, execute, and deliver verified outcomes.

You will work directly with the founding team to develop proprietary AI models and infrastructure, using real-world user interactions and task outcomes to continuously improve model performance. This is a deeply technical role suited to engineers and researchers who have hands-on experience training, fine-tuning, and deploying LLMs rather than simply building applications using third-party LLM APIs.

Requirements

Key Responsibilities

  • LLM Post-Training: Fine-tune open-weight language models such as Llama, Qwen, and Gemma using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement learning techniques.
  • Reinforcement Learning: Design reward functions and optimise model policies using real-world task outcomes, user preferences, and agent performance signals.
  • Agent Data Pipelines: Build high-quality training datasets using agent trajectories, tool interactions, execution traces, and preference signals.
  • Model Evaluation: Develop benchmarks and evaluation frameworks to measure reasoning, tool usage, decision-making, and end-to-end task completion.
  • Training and Inference: Execute distributed model training and high-throughput inference using PyTorch, FSDP, DeepSpeed, vLLM, and SGLang.
  • Production Ownership: Deploy, optimise, monitor, and continuously improve fine-tuned models in production environments.
  • Conduct experiments with model architectures, training methodologies, and optimisation techniques to improve model quality and reliability.
  • Analyse model failures, identify performance bottlenecks, and implement improvements across the training and inference lifecycle.
  • Take end-to-end ownership of model development, from experimentation and training to deployment and ongoing optimisation.

Required Qualifications

  • 8+ years of relevant experience in Data Science, Machine Learning, AI Engineering, or related technical fields.
  • Hands-on experience post-training open-weight LLMs using SFT, DPO, and/or reinforcement learning techniques.
  • Proven experience deploying fine-tuned language models in production environments.
  • Strong proficiency in Python, PyTorch, Transformers, attention mechanisms, and modern LLM architectures.
  • Experience with LoRA/PEFT, distributed training using FSDP or DeepSpeed, mixed-precision training, and multi-GPU infrastructure.
  • Practical experience using vLLM or SGLang for high-throughput inference and agent rollouts.
  • Experience building LLM training datasets, agent trajectories, preference datasets, and model evaluation frameworks.
  • Strong understanding of model optimisation, experimentation, performance analysis, and failure diagnosis.
  • Ability to work independently, solve complex machine learning problems, and take ownership in a fast-paced startup environment.

Good-to-Have Skills

  • Experience with Proximal Policy Optimization (PPO), Group Relative Policy Optimization (GRPO), and Reinforcement Learning from Human Feedback (RLHF).
  • Familiarity with reward modelling and reward-function optimisation.
  • Experience developing or improving long-horizon autonomous AI agents.
  • Exposure to advanced reinforcement learning techniques for improving reasoning, planning, and tool-use capabilities.

Ideal Candidate Profile

The ideal candidate is an engineer or scientist who has actually trained, fine-tuned, and shipped LLMs in real-world environments. You should enjoy solving complex machine learning challenges, experimenting with model architectures and training techniques, and building proprietary AI systems from the ground up.

You should be comfortable working directly with founders, making technical decisions, and taking complete ownership of model development in a fast-moving founding team.

Why Join?

  • Build proprietary AI models and infrastructure from the ground up.
  • Work on challenging problems in LLM post-training, reinforcement learning, and agentic AI.
  • Collaborate directly with experienced AI and product founders.
  • Shape the technical foundation of an AI-powered personal assistant designed to serve millions of users.

Role Details

  • Position: Founding Data Scientist
  • Experience: 8+ Years
  • Location: Gurgaon
  • Work Mode: Work from office, 6 days a week

Must-Have Skills: SFT, LLM

Good-to-Have Skills: PPO, GRPO, RLHF

What this role asks for

Standard for data science / ML roles

LLM · Machine Learning · PyTorch · Python

Common in data science / ML postings at any company.

Compared with Weekday AI's other open jobs.

The team: Weekday's Client via platform HIRES STEADILY

This role is one of 385 open in Weekday's Client via platform. 250 were posted in the last 30 days and the oldest in September: a team that hires all year rather than one being built.

WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS

Python (126 of 385), CI/CD (115 of 385), AWS (107 of 385), SQL (93 of 385), Azure (84 of 385), REST (81 of 385), Agile (71 of 385)

OTHER ROLES ON THIS TEAM

From the team and dates on Weekday AI's own job board.

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