This role pays less than
64%
of similar roles. Most pay
$202,800–$256,950
— the shaded band above.
At the midpoint, this role pays about
$218k
versus about
$230k
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.
As a Senior Machine Learning Engineer on the Trust Frontier AI team, you will develop specialized models and agentic capabilities to protect the community from fraud, including monetary loss, compromised accounts, and property damage. You will own end-to-end machine learning projects, ranging from prototyping solutions for ambiguous problems to productionizing pipelines for batch and real-time use cases. Your daily work involves building abuse behavior detection, developing LLM-powered agents with automated decision-making guardrails, and creating evaluation harnesses to ensure high decision quality. You will utilize Python, TensorFlow, or PyTorch to build models using neural networks, transformers, and gradient boosted trees. The role requires expertise in large-scale data processing, feature engineering, and the development of multimodal models to solve critical trust and safety challenges involving identity, reputation, and fraud detection across various platform interactions.
What does a Machine Learning Engineer earn in Remote?
Median $229700 from 72 postings across 27 companies.
Prototype and develop ML and agentic solutions for ambiguous trust and safety problems.
Design, build, and productionize end-to-end machine learning pipelines for batch and real-time use cases.
Develop specialized models to detect abuse behavior across multiple platform defenses.
Build and iterate on AI agents that automate trust decisions using orchestration and guardrails.
Create benchmarks and evaluation harnesses to measure the quality of model and agent decisions.
Write, review, and ship clean, testable code for training, pipeline improvement, and feature optimization.
Process large-scale structured and unstructured data to improve models for business and operational use cases.
Partner with front-line teams to validate solutions through experiments and quantify impact on key metrics.
What we're looking for
5-10 years of industry experience in applied Machine Learning with a track record of building and productionizing models at scale.
1-2+ years of hands-on experience with LLMs, GenAI technologies, agentic frameworks, orchestration, and evaluation.
Strong programming skills in Python and familiarity with Scala, Java, or equivalent.
Solid understanding of Machine Learning best practices, including training/serving skew minimization, A/B testing, feature engineering, and various algorithms.
Experience with ML frameworks such as TensorFlow, PyTorch, or equivalent.
Experience with data engineering and building end-to-end ML pipelines for batch and real-time systems.
Experience designing evaluation methodology for ML or LLM systems including benchmarks, ground truth, and offline/online metrics.
Bachelor's, Master's, or PhD in CS, ML, or a related field; experience with multimodal models or the Trust and Risk domain (preferred).
Machine Learning
LLMs
GenAI
Agentic AI
Python
Scala
Java
C++
TensorFlow
PyTorch
Kubernetes
Data Engineering
Natural Language Processing
Computer Vision
Anomaly Detection
A/B Testing
Test Driven Development
Generative AI
LLM
PyTorch
Python
Machine Learning
RAG
Reinforcement Learning
Model Quantization
GPU Inference
Data Processing
multimodal modeling
Kernel Development