Machine Learning Engineer / AI Engineer

Traveloka; Jakarta Pusat; On-site; IDR 279,000,000 - 502,200,000; Full time

Job summary

Traveloka's ML Engineering team turns data science models into reliable, production-grade systems — spanning internal automation and tooling as well as ML model deployment. You'll collaborate with Data Scientists and business stakeholders to understand needs and build systems that meet them, with your specific focus shaped by your background and the team's priorities at the time.

Traveloka offers opportunities to own end-to-end ML pipelines, from design through deployment, monitoring, and

Qualifications

• Bachelor's degree in CS, engineering, or related field.
• 3–5 years of ML/Backend or Applied AI engineering experience.
• Proven ability to translate business needs into technical specs.
• Experience deploying ML models in production and building internal tools.

Responsibilities

• Collaborate with Data Science and business stakeholders to understand technical and operational needs.
• Translate requirements into well-architected, maintainable systems.
• Build, deploy, and maintain production systems (automation or ML serving).
• Ensure systems you own are reliable, monitored, and scoped to business needs.
• Manage projects from design through to production, working independently on ambiguous problems.

Skills

Python

SQL/BigQuery

Cloud platforms

System design

Communication

ML concepts

Education

Bachelor's in Computer Science, Engineering, or related field

Tools

Docker

MLflow

Airflow

Job description

About The Role

Traveloka's ML Engineering team turns data science models and internal ideas into reliable, production-grade systems — spanning internal automation and tooling as well as ML model deployment. You'll collaborate with Data Scientists and business stakeholders to understand needs and build systems that meet them, with your specific focus shaped by your background and the team's priorities at the time.

Job Description

Traveloka's ML Engineering team turns data science models and internal ideas into reliable, production-grade systems — spanning internal automation and tooling as well as ML model deployment. You'll collaborate with Data Scientists and business stakeholders to understand needs and build systems that meet them, with your specific focus shaped by your background and the team's priorities at the time.

Key Responsibilities

• Collaborate with Data Science and business stakeholders to understand technical and operational needs.
• Translate requirements into well-architected, maintainable systems.
• Build, deploy, and maintain production systems (e.g. internal automation and tooling, or ML model serving infrastructure, depending on focus).
• Ensure systems you own are reliable, monitored, and scoped to business needs.
• Manage projects from design through to production, working independently on ambiguous or loosely-defined problems.
  
  

Qualifications

• Bachelor's in Computer Science, Engineering, or related field.
• 3–5 years of experience in ML Engineering, Backend Engineering, or Applied AI Engineering.
• Strong ability to translate business/operational needs into technical specifications.
• Experience in building internal tools, bots, or workflow automation (e.g. integrating with ticketing systems, internal APIs, chat interfaces)
• Experience in deploying and serving ML models in production (API frameworks, monitoring, scaling)
• Familiar with data pipeline design and orchestration
• Strong Python; proficient in SQL/BigQuery for data access and analysis.
• Experience with cloud-based tech stacks and Docker (GCP preferred).
• Proactive, self-motivated, comfortable owning systems independently.
• Exceptional communication skills, including explaining technical tradeoffs to non-technical stakeholders.
• Comfortable working alongside AI tools (eg. Claude Code) to accelerate model development and analysis.
  
  

Good to Have

• Experience productionizing LLM or embedding-based systems.
• Familiarity with MLOps tooling (MLflow, Airflow).
• Experience with A/B testing or experimentation platforms.
• Travel, Hospitality, or OTA experience.