below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$207,918–$254,750
— the blue band above.
At the midpoint, this role pays about
$214k
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.
The Senior Machine Learning Engineer, Trust joins the Trust Frontier AI team to develop innovative technology protecting the community from fraud and safety risks. You will own end-to-end machine learning projects, framing ambiguous problems to build specialized models, AI agents, and evaluation harnesses. Day-to-day responsibilities include prototyping solutions, designing production pipelines for batch and real-time use cases, and improving abuse behavior detection. You will utilize Python, Scala, and Java alongside frameworks like TensorFlow and PyTorch to implement neural networks, transformers, and gradient boosted trees. The role requires expertise in LLMs, GenAI, and agentic frameworks to solve complex trust issues such as identity, reputation, and monetary loss. You will work with large-scale structured and unstructured data to ensure platform integrity and user safety.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 companies.
Frame and prototype ML and agentic solutions for ambiguous trust and safety problems.
Design, build, and productionize end-to-end ML pipelines for batch and real-time use cases.
Build and improve abuse behavior detection systems that generalize across multiple defenses.
Design and iterate on AI agents to automate trust decisions with appropriate guardrails.
Create benchmarks, evaluation harnesses, and instrumentation to measure model decision quality.
Develop specialized models for trust and safety using LLMs and AI agents.
Write, review, and ship clean, testable code for model training and pipeline optimization.
Partner with defense teams to validate solutions through experiments and quantify business impact.
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 and GenAI technologies, including building with agentic frameworks, orchestration, and evaluation.
Strong programming skills in Python and familiarity with Scala, Java, or equivalent.
Solid understanding of ML best practices, including training/serving skew, A/B testing, feature engineering, and algorithms like transformers and neural networks.
Experience with ML frameworks and tooling such as TensorFlow, PyTorch, or equivalent.
Experience with data engineering and building end-to-end ML pipelines for both batch and real-time systems.
Experience designing evaluation methodology for ML or LLM systems, including benchmarks, ground truth, and offline/online metrics.
A Bachelor's, Master's, or PhD in CS/ML or a related field.
Experience with multimodal models (vision, document, or speech) is a plus (preferred).
Exposure to the Trust and Risk domain (e.g., fraud detection, identity) is a plus (preferred).
Machine Learning
C++
Python
PyTorch
TensorFlow
Computer Vision
Multi-sensor Fusion
Deep Learning
TensorRT
Transformers
Data Pipelines
Model Optimization
NVIDIA DRIVE AV
Object Detection
Semantic Segmentation