Lead end-to-end AI research, development, evaluation, and production deployment for ecommerce search, retrieval, ranking, personalization, computer vision, and multimodal discovery. Build and optimize multimodal models and pipelines, establish evaluation and MLOps practices, and deliver GenAI and agentic systems. Provide technical leadership through architecture decisions, mentoring, research direction, and cross-functional collaboration while ensuring scalable, reliable, observable, and cost-efficient ML operations.
Role Overview
We are hiring a Lead AI Engineer / Lead Applied Scientist (G4) to provide hands-on technical leadership for AI systems powering ecommerce search, retrieval, ranking, personalization, and visual discovery. This is a senior individual-contributor role centered on research, rapid experimentation, production engineering, and measurable business impact across text, image, and multimodal experiences.
You will set technical direction and lead end-to-end AI solutions across matching, multimodal retrieval, ranking, computer vision, vision-language modeling, and GenAI-powered experiences—from problem framing and research through deployment, evaluation, and iteration at scale. You will also mentor engineers and scientists, establish reusable technical patterns, and influence cross-functional product and platform roadmaps.
Core Responsibilities
• Set technical direction and lead research, experimentation, and production delivery for ML models spanning:
o Product matching and entity resolution
o Semantic, visual, and multimodal retrieval
o Search relevance, ranking, re-ranking, and recommendation
o Image classification, detection, segmentation, similarity, visual attribute extraction, and content understanding
• Architect and optimize multi-stage retrieval and ranking pipelines across text, image, and fused multimodal representations.
• Develop, fine-tune, and evaluate computer vision, vision-language, and multimodal models, including image/text encoders, multimodal embeddings, contrastive learning approaches, and vision-language foundation models.
• Create rigorous offline evaluation, benchmarking, error analysis, and online experimentation frameworks; define success metrics and translate model improvements into customer and business outcomes.
• Apply reinforcement learning, learning-to-rank, or bandit methods where appropriate for personalization and ranking optimization.
• Build and deploy GenAI and agentic AI systems that reason across text and visual content for search, discovery, catalog, and content use cases.
• Lead model and data strategy for multimodal systems, including training data quality, annotation, weak supervision, hard-negative mining, robustness, bias, safety, and responsible use of visual data.
• Productionize ML systems using Databricks-based pipelines and AKS, designing for latency, throughput, reliability, observability, and cost efficiency.
• Establish reusable architectures, evaluation standards, and MLOps/LLMOps practices that improve delivery quality and engineering velocity across the team.
• Independently frame ambiguous, high-impact problems and drive them from research hypothesis through production adoption.
• Provide hands-on technical leadership through design reviews, mentoring, technical decision-making, and collaboration with Product, Engineering, Data, and business stakeholders.
Requirements
Required Qualifications
• 6+ years of experience in machine learning, applied science, ML engineering, or applied AI, including experience leading technically complex initiatives from research through production.
• 2+ years of hands-on experience developing, training, fine-tuning, and evaluating computer vision models for production or applied research use cases.
• Deep expertise in at least two of the following, with the ability to work across the broader portfolio:
o Search, information retrieval, learning-to-rank, recommendation, or entity matching
o Computer vision and deep visual representation learning
o Vision-language models, multimodal embeddings, or multimodal foundation models
• Hands-on experience designing and evaluating image, text, and multimodal retrieval or ranking systems, including relevant datasets, loss functions, sampling strategies, benchmarks, and error analysis.
• Hands-on experience developing and evaluating ranking and re-ranking models, including learning-to-rank, neural or transformer-based rankers, cross-encoders, feature-based models, hard-negative mining, and offline and online relevance evaluation.
• Strong knowledge of modern deep learning architectures and training or adaptation techniques, including transformers, contrastive learning, transfer learning, fine-tuning, and embedding-based methods.
• Proven track record of building and operating production-grade ML systems with measurable product or business impact.
• Strong proficiency in Python and PyTorch or an equivalent deep learning framework.
• Hands-on experience with Databricks, distributed data/model pipelines, and Kubernetes-based deployment; AKS experience is preferred.
• Demonstrated technical leadership: setting direction, making architecture decisions, raising scientific and engineering standards, mentoring others, and influencing cross-functional stakeholders without relying on formal authority.
• Ability to communicate complex research and system trade-offs clearly to technical, product, and business audiences.
Preferred Qualifications
Master’s or PhD in Computer Science, Machine Learning, Computer Vision, NLP, Information Retrieval, or a related field, or equivalent practical experience.
• Experience with:
o Vision transformers, image-text models, multimodal large language models, or models such as CLIP-like dual encoders
o Visual search, image similarity, multimodal product matching, OCR/document understanding, or product attribute extraction
o Large-scale approximate nearest-neighbor retrieval, vector databases, multi-stage ranking, and recommendation systems
o Model adaptation, distillation, quantization, or efficient multimodal inference
o GenAI, LLMs, retrieval-augmented generation, and agentic frameworks
o Ecommerce, marketplace, catalog, or digital merchandising domains
• Familiarity with:
o Modern data platforms, data lakes, and scalable GPU training or inference environments
o MLOps/LLMOps tools such as MLflow
o AI governance, evaluation, explainability, safety, privacy, and responsible use of image and multimodal data
• Evidence of technical thought leadership through patents, publications, open-source contributions, internal platform leadership, or repeated delivery of novel AI capabilities is a plus.
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