Founding Data Scientist
Weekday AI (company site, opens in a new tab)8+ yrsGurugramOn-siteDirect career pageEst. 40–65 LPA
Posted 23h ago
Reach people at Weekday AI
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Weekday AI you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own the entire lifecycle of large language model (LLM) improvement including data creation, reward design, fine-tuning, reinforcement learning, evaluation, and production deployment.
- Develop and deploy proprietary AI models for autonomous agents assisting in real-world task completion such as calls, bookings, and payments.
- Build and optimize training data pipelines, evaluation benchmarks, and conduct experiments to improve model quality and inference performance in production.
Minimum Requirements
- 8+ years of experience in Data Science, Machine Learning, or AI Engineering.
- Hands-on experience with post-training open-weight LLMs using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement learning techniques.
- Proven production deployment experience of fine-tuned LLMs with proficiency in Python, PyTorch, Transformers, and distributed training tools like FSDP or DeepSpeed.
- Location requirement: Gurgaon; Work mode: Work from office, 6 days a week.
Ideal Candidate Profile
- Experienced engineer or researcher who has trained, fine-tuned, and shipped LLMs in real-world production environments.
- Able to independently solve complex ML problems, experiment with architectures and training methods, and act with end-to-end ownership in a fast-paced startup.
- Comfortable making technical decisions and working directly with founding team to shape AI agents for large-scale autonomous tasks.