Data Scientist (Foundational Models) – Intelligence Engine (Agentic AI)
On-siteFull-timeMid · 2-6 yearsH1B likely
About this role
About SingleInterface
At SingleInterface, we’re building an AI Retail Tech Platform for multi-location brands, helping them win
local discovery, engagement, conversion, and measurable business outcomes.
Our Vision
Making AI-driven solutions simple and accessible for hyperlocal businesses to manage complex digital
marketing needs.
Our Goal
Fuel growth for 10 million business locations by 2030 through advanced AI-powered frictionless
experiences.
Core Values
Customer First • Getting Things Done • Being Authentic • Being Finicky • Being Techurious
Role Summary
You’ll help build the Intelligence Engine: the learning layer of our platform that improves itself over time
using data, experiments, and models. This role is for people who don’t just run notebooks — they ship
outcomes.
Key Expectations
- Build and improve foundational ML models powering discovery, ranking, relevance, and conversion signals.
- Curate datasets (structured and unstructured), define labeling strategies, and own feature/model iterations end-to-end.
- Run model experiments: offline evaluation, online experimentation (A/B), and rapid iteration loops.
- Design evaluation frameworks for LLMs, embeddings, retrieval, and agent decisioning, including human-in-the-loop checks where needed.
- Partner tightly with Product and Engineering to translate ambiguous problems into measurable model wins.
Technical Requirements (What you should be strong at)
- Model training: classical ML and deep learning (PyTorch/TensorFlow), loss functions, regularization, calibration, bias/variance trade-offs.
- Representation learning: embeddings, metric learning, retrieval, similarity search, vector databases (or equivalent).
- LLM-related workflows: fine-tuning (as applicable), prompt and retrieval strategies, evaluations, hallucination checks, guardrails.
- Ranking and personalization: learning-to-rank, recommender patterns, propensity models (bonus).
- Experimentation: strong statistical thinking, causal intuition, offline-to-online translation, metric design.
- Data fluency: SQL and Python, feature engineering, data quality checks, pipeline sanity.
- Bonus: RL or bandits (explore-exploit), multi-agent evaluation or orchestration metrics.
Qualifications and Experience
- 2–6 years building and training ML models that made it to production and moved a business metric.
- Strong fundamentals in ML, math, and statistics; you can reason about trade-offs, not just copy architectures.
- Comfortable with ambiguity, fast iteration, and high ownership.
What’s on offer
- High-ownership role building a core “brain” for the platform.
- Work with strong Product and Engineering teams, a fast shipping culture, and real-world scale.
- In-office first team environment in Gurgaon.