About TravelokaTraveloka is Southeast Asia’s leading all-in-one travel platform connecting millions globally with trusted, world-class experiences. With over 140 million app downloads and more than 40 million monthly active users, Traveloka stands as one of the most popular travel apps in the region.

Job Description

Job ID: MJ000229

Role Overview

As a Senior Machine Learning Engineer, you will design, build, and scale production-grade algorithmic solutions that directly impact our digital commerce platform. Operating at the intersection of deep learning, reinforcement learning, and distributed computing, you will tackle complex real-world challenges—ranging from real-time dynamic pricing and personalization to generative AI travel assistants and demand forecasting. You will collaborate closely with cross-functional product and engineering teams to deploy low-latency, high-throughput models that serve millions of users and drive measurable business impact.

Key Responsibilities

• Dynamic Pricing & Revenue Management: Design and implement reinforcement learning (RL) and contextual bandit algorithms to optimize real-time pricing, personalized discounts, and multi-product bundling strategies.
• Search, Ranking & Personalization: Develop advanced deep learning architectures (e.g., Two-Tower models, Graph Neural Networks, and Transformers) to rank offerings, inventory, and experiences based on real-time user intent, historical behavior, and high-dimensional contextual signals.
• Generative AI & Conversational Commerce: Build and fine-tune Large Language Models (LLMs) to power next-generation AI assistants, automating complex itinerary planning and multi-modal customer support.
• Supply-Demand Forecasting: Construct high-precision spatio-temporal forecasting algorithms to predict demand surges, booking 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 and transactional records.
• 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

• 8+ years of professional experience building, deploying, and scaling advanced machine learning algorithms in a high-throughput, customer-facing digital economy.
• Master’s or Ph.D. in Computer Science, Operations Research, Applied Mathematics, Data Science, or a highly quantitative field.
• 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).
• Domain Specifics: Proven track record in at least two of the following mathematical/algorithmic domains:
• Learning-to-Rank (LTR) frameworks and collaborative filtering.
• Multi-armed bandits and Reinforcement Learning (QQQ-learning, policy gradients) applied to pricing or allocation.
• Time-series forecasting at scale (DeepAR, Prophet, or custom neural forecasting architectures).
• Large-scale anomaly detection/classification for payment and account fraud.
• Big Data & Cloud Infrastructure: Strong hands-on experience working with Spark, Kafka/Flink, Airflow, and cloud ecosystems (AWS, GCP, or Azure).