Principal Machine Learning Engineer, LLM Fine-tuning and Optimization

Airbnb

Confirmed live today Trusted

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $292,000–$365,000 / yr
Posted: 156 days ago
Freshness: Confirmed live today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $231k

This role $328k

$160k $387k

below market most similar roles pay here above market

This role pays more than 95% of similar roles. Most pay $207,975–$254,750 — the blue band above. At the midpoint, this role pays about $328k versus about $231k 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 46 open roles on FindRole.

Listed pay typically runs $206,000–$255,000 across 44 roles with salary data.

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At a glance

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

Role Posting Log in to save

The Principal Machine Learning Engineer- LLM Fine-tuning and Optimization joins the CS AI product team to drive customer support initiatives using generative AI technologies. This role involves fine-tuning state-of-the-art large language models for diverse use cases, including AI assistants, autonomous agents, and travel planning tools. You will build and productionize LLM models and pipelines at scale, handling both batch and real-time use cases while optimizing for high-performance deployment on ML infrastructure. Key responsibilities include creating multi-year tech roadmaps, evaluating foundational models, and managing data processing for fine-tuning, alignment, and reinforcement learning. Required skills include Python, PyTorch, and experience with model quantization, compression, and kernel development. You will solve complex technical problems involving large-scale structured and unstructured data to enhance customer support systems and overall product performance.

What does a Machine Learning Engineer earn in Remote?

Median $246638 from 41 postings across 11 companies.

See salary data

Skills

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

What you'll do

Fine-tune state-of-the-art LLMs for diverse use cases and optimize them for high-performance deployment.
Build and continuously improve foundation models using large-scale structured and unstructured data.
Create a multi-year technology roadmap to keep the team at the leading edge of AI.
Evaluate and select high-quality foundational models to enhance performance and efficiency.
Prototype, develop, and productionize LLM models and pipelines for batch and real-time use cases.
Drive key AI architectural decisions for products and contribute to the company's ML platform strategy.
Design and develop AI products such as AI Assistants, autonomous agents, 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.
Programming experience in Python and hands-on experience with frameworks such as PyTorch.
Proven record of training, fine-tuning, optimizing models, and inference run-time.
Post-training experience in areas like data processing for fine-tuning, LLM alignment, reinforcement learning, or model evaluation.
Specialized experience in runtime optimizations, model quantization, compression, on-device inference, or kernel development.
Publications at peer-reviewed AI conferences (preferred).
Experience with AI in customer support, agile AI production lifecycles, or scaling business-critical AI services (preferred).

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