Principal Machine Learning Engineer, LLM Fine-tuning and Optimization

Airbnb

Confirmed live yesterday Trusted

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $292,000–$365,000 / yr
Posted: 101 days ago
Freshness: Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $234k

This role $328k

$163k most similar roles pay here $387k

This role pays more than 97% of similar roles. Most pay $211,200–$256,950 — the shaded band above. At the midpoint, this role pays about $328k versus about $234k for comparable roles.

Based on 240 similar postings.

Employer

About Airbnb

Founded in 2008 and formerly known as AirBed & Breakfast, Inc., Airbnb is a global marketplace connecting travelers with hosts who offer unique accommodations, ranging from private rooms to entire homes. It operates a massive digital platform for booking stays, experiences, and travel services worldwide.

Airbnb currently has 65 open roles on FindRole.

Listed pay typically runs $200,000–$240,000 across 51 roles with salary data.

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At a glance

TL;DR · Principal Machine Learning Engineer, LLM Fine-tuning and Optimization

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As a Principal Machine Learning Engineer on the CS AI product team, you will drive initiatives to integrate generative AI technologies into customer support systems. You will be responsible for fine-tuning state-of-the-art large language models, optimizing them for high-performance deployment on internal infrastructure, and developing products like AI assistants and autonomous agents. Your daily work involves processing large-scale structured and unstructured data, creating multi-year technical roadmaps, and productionizing LLM pipelines for both batch and real-time use cases. You will utilize Python and PyTorch while applying expertise in model quantization, compression, reinforcement learning, and alignment. The role focuses on solving complex problems in the customer support domain by building scalable tools for travel planning and recommendation systems while ensuring high-quality performance through rigorous evaluation and automated testing of foundational models.

What does a Machine Learning Engineer earn in Remote?

Median $229700 from 72 postings across 27 companies.

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Skills

Generative AI LLM Fine-tuning PyTorch Python RAG Machine Learning Reinforcement Learning Model Quantization GPU Inference Kernel Development Agile ML Infrastructure Data Processing Multimodal Modeling

What you'll do

Fine-tune state-of-the-art LLMs for diverse use cases while optimizing them for high-performance deployment on internal infrastructure.
Develop and productionize LLM models and pipelines at scale for both batch and real-time applications.
Create a multi-year technical roadmap to keep the team at the forefront of evolving AI technologies.
Evaluate and select high-quality foundational models to ensure performance and efficiency across various products.
Drive key architectural decisions for AI products and contribute to the overall ML platform strategy.
Process large-scale structured and unstructured data to build and improve foundation models for business use cases.
Design and develop AI products such as autonomous agents, recommendation systems, and travel planning tools.

What we're looking for

PhD in Computer Science, Machine Learning, Mathematics, Statistics, or a related technical field.
10+ years of experience developing machine learning models and products at scale from inception to business impact.
Proficiency in Python programming and hands-on experience with frameworks such as PyTorch.
Proven track record in training, fine-tuning, optimizing models, and improving inference run-time.
Experience in post-training areas including data processing for fine-tuning, LLM alignment, reinforcement learning, and model evaluation.
Specialized experience in runtime optimizations, model quantization, compression, or kernel development.
Experience deploying and scaling business-critical AI services on ML infrastructure.
Experience with the full AI product lifecycle using Agile practices in an Applied AI/ML domain.

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