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Palazzo · 2 months ago

Applied ML Scientist — Model Calibration & Personalization

New York City, NY

Full-time

Hybrid

Mid, Senior Level

$130K/yr - $170K/yr

4+ years exp

Palazzo is an AI-powered interior design platform seeking an Applied ML Scientist to transform their heuristic recommendation system into a data-driven personalization engine. The role involves refining existing models, creating context embeddings, and establishing a continuous learning loop to improve user engagement and satisfaction.

ArchitectureArtificial Intelligence (AI)Interior Design

Responsibilities

Fine-tune existing CLIP/SigLIP embeddings using weak supervision from heuristic scores and user feedback

Learn weight calibration functions for cohesion scoring (style, color, material, budget)

Train and deploy lightweight ranking or regression models (XGBoost, MLPs) that learn from clickstream data

Create context embeddings for rooms and users from uploaded photos, design choices, and interactions

Implement preference clustering or per-user fine-tuning of aesthetic weights

Define offline ranking metrics (NDCG, coverage, novelty, diversity, cohesion score stability)

Design and interpret online A/B tests to quantify engagement and revenue lift

Collaborate with PM/designer to align metrics with user experience goals

Work with data labelers and internal designers to curate labeled sets for compatibility and bundle cohesion

Develop lightweight active learning loops that prioritize uncertain or low-confidence recommendations for review

Build dashboards summarizing model health, drift, and performance over time

Qualification

Recommendation systemsMetric learningRanking modelsPyTorchXGBoost / LightGBMMultimodal embeddingsOffline evaluation metricsOnline experimentationVisual design cohesionAnalyticalStartup builder mentalityEmpiricalPragmaticVisually literateCollaborative

Required

4–8 years applied ML in recsys, personalization, or multimodal AI

Strong in recommendation systems, metric learning, or ranking models

Deep familiarity with PyTorch, Faiss, XGBoost / LightGBM, and the Python data stack

Hands-on experience fine-tuning multimodal embeddings (CLIP, SigLIP, BLIP, etc.)

Solid understanding of offline evaluation metrics (AUC, NDCG, recall@K, diversity) and online experimentation (A/B, multi-armed bandits, significance)

Comfort reasoning about visual design cohesion — palette, texture, style, material — not just numeric similarity

Designs experiments that clearly link model changes to business KPIs

Explains results clearly to product and design stakeholders — translates 'model lift' into 'conversion lift'

Thrives in fast-paced, scrappy environments

Works iteratively: prototypes → measures → deploys

Can operate independently with limited engineering support

MS/PhD preferred in ML, CS, Applied Math, or related field

Experience in e-commerce, fashion tech, home design, or digital advertising

Empirical, pragmatic, visually literate, collaborative with design/PM