Our Client is a fast-growing AI-powered retail technology company transforming how multi-location brands connect with customers in an increasingly digital world. Backed by leading global investors, our platform helps businesses manage their digital presence, engage customers intelligently, strengthen online reputation, and integrate store-level products and services across channels. Through data, automation, and AI-driven insights, we enable brands to deliver exceptional customer experiences and accelerate growth at scale.

● 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: -

● 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.

● 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.

● A platform for you to grow yourself with maximum speed with zero obstacles and make real impact.

● We are an in-office first org — because no great idea ever started with “You’re on mute.” We thrive on hallway high-fives, spontaneous brainstorms, and the kind of teamwork that just hits different when you're in the room.