Senior Machine Learning Engineer

Role Overview

We are establishing a brand-new, high-impact AI & Algorithm Domain within our core Business Unit. As a founding Senior Algorithm Engineer, you will drive our next-generation Generative AI capabilities (such as customer service chatbots and translation agents) and architect/optimize core algorithmic engines, including Search, Recommendation, Advertising, and Dynamic Pricing.

This is a rare, high-ownership role where your algorithms will directly impact millions of travelers-optimizing user experience, conversion rates, and revenue management from day one.

Key Responsibilities

Dynamic Pricing & Revenue Management: Design and implement reinforcement learning (RL) and contextual bandit algorithms to optimize real-time pricing, personalized discounts, and hotel/flight bundling.

Search, Ranking & Personalization: Develop advanced deep learning architectures (e.g., Two-Tower models, Graph Neural Networks, and Transformers) to rank properties, flights, and experiences based on real-time user intent, historical behavior, and high-dimensional destination context.

Generative AI & Conversational Commerce: Build and fine-tune Large Language Models (LLMs) to power next-generation AI travel assistants, automating complex itinerary planning and multi-modal customer support.

Supply-Demand Forecasting: Construct high-precision spatio-temporal forecasting algorithms to predict travel demand surges, hotel cancellation probabilities, and market competitiveness.

Low-Latency Inference: Optimize complex deep learning models to execute within milliseconds, ensuring real-time search results are served seamlessly during peak traffic periods.

Distributed Infrastructure: Architect and scale distributed training pipelines using frameworks like PyTorch, Ray, or DeepSpeed over massive datasets consisting of billions of real-time clickstream events.

Algorithmic Guardrails: Implement rigorous evaluation frameworks, multi-variant A/B testing systems, and counterfactual reasoning methods to measure the true causal impact of algorithmic changes on conversion rates and Gross Bookings Value (GBV).

Required Qualifications

Experience & Education:

Professional Experience: 8+ years of professional experience building, deploying, and scaling advanced machine learning algorithms in a high-throughput, customer-facing digital economy.

Education: Master's or Ph.D. in Computer Science, Operations Research, Applied Mathematics, Data Science, or a highly quantitative field.

Technical Skills:

Programming Mastery: Expert proficiency in Python or SQL with a focus on writing production-grade, highly optimized code.

ML / AI Ecosystem: Deep expertise in PyTorch, TensorFlow/JAX, and classical ML stacks (Scikit-Learn, XGBoost, LightGBM).

Big Data & Cloud Infrastructure: Strong hands-on experience working with Spark and Kafka.

Domain Expertise:

Proven track record in at least two of the following mathematical/algorithmic domains:

Learning-to-Rank (LTR): Frameworks and collaborative filtering.

Reinforcement Learning: Multi-armed bandits and RL (Q-learning, policy gradients) applied to pricing or allocation.

Time-Series Forecasting: Forecasting at scale using DeepAR, Prophet, or custom neural forecasting architectures.

Anomaly Detection: Large-scale anomaly detection/classification for payment and account fraud.