Wizard

Machine Learning Engineer

• Own and evolve our multi-engine inference platform, supporting a variety of model types and serving requirements.
• Build and improve production ML pipelines — taking models from experimentation to reliable, high-throughput serving.
• Define and implement model versioning, rollout, rollback, and lifecycle management strategies that ensure reproducibility and operational reliability.
• Define and enforce serving-layer SLAs, including latency, availability, GPU utilization, Time-to-First-Token (TTFT), and Inter-Token Latency (ITL).
• Build observability, monitoring, alerting, and operational tooling for production inference systems.
• Apply software engineering best practices, including testing, CI/CD integration, and reproducibility across ML workflows.
• Optimize inference performance through efficient resource utilization, hardware-aware serving strategies, and cost-conscious infrastructure design.
• Ensure ML serving systems are secure, scalable, and operationally resilient.
• Partner with ML, Data, Product, and DevOps teams to turn ideas into production systems, driving the technical decisions on serving and scale.

Senior Machine Learning Engineer – Inference Platform

Research Scientist

• Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes)
• Design and run experiments to measure improvements and regressions
• Build and maintain evaluation datasets, benchmarks, and scoring frameworks
• Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning where it matters
• Translate ambiguous product questions into clear, measurable hypotheses and analysis
• Partner with ML Engineers to validate model changes and guide iteration
• Identify failure modes and edge cases, and drive improvements through data
• Make agent performance visible, trusted, and actionable across product and engineering

AI Applied Scientist

• Own and evolve the hybrid search pipeline – lexical retrieval, dense vector search, reciprocal rank fusion, and multi-stage reranking on Elasticsearch
• Build and train adaptive retrieval models – lightweight classifiers and ranking models that configure search behavior per query, per category, per context (source routing, per-attribute boost prediction, filter mode decisions)
• Design and productionize the learning-to-rank system – from feature engineering through model training (LightGBM, ONNX) to production deployment and A/B evaluation
• Build the search feedback loop – instrument and integrate behavioral signals (CTR, conversions, add-to-cart) into ranking and retrieval as features for LTR, reward signals for adaptive retrieval, and inputs for search-side personalization
• Build the business and ordering layer – separating organic relevance from sponsored/partner placement with quality gates, slot allocation, campaign configuration, and an auction-style approach as the system matures
• Own the offline enrichment pipeline – LLM-based product enrichment at scale, data quality monitoring, and index management
• Instrument and evaluate everything – bulk evaluation pipelines, per-category metric tracking, regression detection, experiment analysis
• Integrate query understanding outputs into retrieval – translating extracted attributes, intents, and constraints into filters, boosts, and retrieval strategy decisions

Senior Machine Learning Engineer – Inference Platform

AI Applied Scientist

Machine Learning Engineer – Search & Retrieval Systems