below marketmost similar roles pay hereabove market
This role pays more than
83%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3546 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2746 roles with salary data.
Sr. Machine Learning Engineer, ML Systems Evaluation Engineering joins the SCALE team within the Siri organization to evaluate AI and ML models at scale. This role focuses on delivering offline evaluation insights to improve the end-user experience while maintaining strict privacy standards. You will collaborate with cross-functional teams to curate high-quality evaluation datasets for state-of-the-art models, specifically addressing challenges in highly personalized user experiences. Key responsibilities include leveraging Large Language Models for LLM-as-a-Judge automated evaluations, assessing the naturalness of digital assistant conversations, and using generative AI to create adversarial scenarios for edge cases. Required skills include Python programming, experience managing large-scale datasets for ML training, and deep domain knowledge in Conversational AI, systems engineering, and the end-to-end ML product lifecycle.
What does a Machine Learning Engineer earn in California?
Median $236000 from 195 postings across 34 companies.
Use Large Language Models to automatically evaluate model changes and assess conversation naturalness.
Harness generative AI to create adversarial scenarios that anticipate edge cases.
Curate and evolve high-quality evaluation datasets for state-of-the-art models.
Deliver offline evaluation insights to drive model development and improve user experience.
Define and measure evaluation coverage for Large Language Models and agentic systems.
Manage large-scale datasets for machine learning training and evaluation.
Develop scalable data solutions specifically for natural language products.
What we're looking for
7+ years of professional experience applying machine learning to real-world problems and crafting scalable data solutions for natural language products.
Proven experience managing large-scale datasets for ML training and/or evaluation.
Excellent programming skills in Python.
MS/PhD in Machine Learning, Computer Science, or equivalent experience in a related field.
Deep domain knowledge in Conversational AI and a strong understanding of the end-to-end ML product lifecycle (preferred).
Expertise in defining and measuring evaluation coverage for Large Language Models (LLMs) and agentic systems (preferred).
Track record of delivering large-scale, cross-functional ML product or platform outcomes (preferred).
Experience with systems engineering and in-depth understanding of ML and SW component interdependencies (preferred).
Python
Large Language Models
LLM-as-a-Judge
Generative AI
Conversational AI
Natural Language Understanding
Machine Learning
Systems Engineering
Statistical Evaluation
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Natural Language Processing
Deep Learning
Computer Vision
Machine Learning
Adversarial Training
Content Moderation
Search Ranking
Retrieval Pipelines