Team Lead / Principal ML Engineer

Aviasales; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Aviasales seeks a Principal ML Engineer or Team Lead to drive ML projects for their travel search engine. The role focuses on deploying production models, building MLOps infrastructure, and mentoring a team of engineers.

You will move models from research to production, work with Python and classical ML, and ensure robust monitoring and scalable data pipelines. You will articulate technical decisions with business impact and guide architectural choices.

Qualifications

• Experience shipping production ML systems and moving models from research to production.
• Ability to mentor and lead a team of engineers.
• Strong Python programming and ML model development skills.

Responsibilities

• Lead the development and deployment of ML models for a high-traffic travel search engine.
• Build and maintain MLOps infrastructure, data pipelines and orchestration (Airflow/Kubeflow).
• Establish robust monitoring, logging and performance dashboards for ML systems.
• Translate model performance metrics into business outcomes like conversions and revenue.
• Mentor team members and communicate technical decisions with business impact.

Skills

Python

Machine Learning

MLOps

Team leadership

Production deployment

Data pipelines

Tools

Airflow

Kubeflow

LLM APIs

Transformers

Job description

Aviasales seeks a Principal ML Engineer or Team Lead to drive ML projects for their travel search engine. This role focuses on deploying production models, building MLOps infrastructure, and mentoring a team, requiring strong Python, classical ML, and practical MLOps experience.

This role involves leading the development and deployment of machine learning models for a high-traffic travel search engine. The focus is on moving models from research to production, ensuring they perform reliably and efficiently. This includes hands-on work with Python, classical ML techniques, and text-based neural networks like BERT-like transformers and LLM APIs, alongside robust MLOps practices.

A significant part of the work involves building and maintaining the infrastructure that supports these ML systems. This includes data pipelines, orchestration using tools like Airflow or Kubeflow, experiment tracking, and ensuring proper logging and monitoring. The expectation is a deep understanding of how high-load services operate, extending beyond just the ML models themselves.

Success in this position means not only delivering effective ML solutions but also translating model performance metrics into business outcomes like conversions and revenue. The role also carries a leadership component, requiring the ability to mentor team members and articulate technical decisions with a clear understanding of their financial impact.