below marketmost similar roles pay hereabove market
This role pays more than
68%
of similar roles. Most pay
$162,000–$237,250
— the blue band above.
At the midpoint, this role pays about
$214k
versus about
$200k
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
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
JOB TITLE: AI Engineer – Algorithm Evaluation & Agentic Systems
The AI Engineer – Algorithm Evaluation & Agentic Systems joins the DAQ team to evaluate and elevate advanced visual technologies. This role focuses on benchmarking and integrating state-of-the-art models for image and video understanding. You will design and scale comprehensive evaluation pipelines, perform deep failure analysis to identify visual hallucinations or temporal inconsistencies, and architect advanced agentic systems for multi-step user problems like video summarization. Key responsibilities include curating high-quality golden datasets and providing data-driven insights to model training teams. Required skills include proficiency in Python and PyTorch, deep knowledge of Computer Vision, Vision Transformers, and Vision-Language Models, along with experience in agentic workflows involving tool use, planning, and memory management. You will solve problems regarding AI safety, robustness, and multi-modal evaluation.
What does a AI Engineer earn in California?
Median $246150 from 100 postings across 17 companies.
Design, build, and scale comprehensive evaluation pipelines for image and video understanding tasks.
Perform deep failure analysis to identify root causes of visual hallucinations and temporal inconsistencies.
Build and deploy agentic workflows that use vision models to solve multi-step user problems.
Isolate and triage specific failures within agentic workflows such as tool selection and memory retrieval.
Lead the strategy for curating high-quality, schematized datasets and ground-truth benchmarks for multi-modal capabilities.
Provide data-driven insights and metrics to core model training teams to guide model fine-tuning.
Define robust metrics and KPIs for generative AI using automated regression testing and LLM-as-a-judge methodologies.
What we're looking for
Master's degree and a minimum of 3 years of relevant industry experience.
3+ years of applied experience in Machine Learning, Computer Vision, or AI System Evaluation.
Deep understanding of core Machine Learning principles including probability, statistics, data distributions, and model bias/variance.
Deep theoretical and practical understanding of Computer Vision and Vision-Language Models, including Vision Transformers and spatial-temporal modeling.
Proven track record of defining robust metrics and designing evaluation frameworks for generative AI or foundation models.
Experience building and evaluating LLM/VLM-powered agents, including tool use, multi-step reasoning, planning, and memory management.
Strong proficiency in Python and experience with deep learning frameworks like PyTorch for building scalable evaluation pipelines.
Demonstrated ability to lead technical evaluation strategies, drive architectural decisions, and mentor engineers (preferred).
Machine Learning
Python
C++
NLP
Information Retrieval
Recommendation Systems
Data Generation
Data Quality Assessment
Online Metrics
Offline Metrics
Search Systems
Multi-modal Data
Production ML Systems
Machine Learning
Python
C++
NLP
Information Retrieval
Recommendation Systems
Data Generation
Data Quality Assessment
Online Metrics
Offline Metrics
Production ML Systems
Multi-modal Data
Python
Java
C++
JavaScript
SQL
Microsoft Azure
Kubernetes
Docker
React.js
Agile Methodology
DevOps
Microservices
API
Full Stack Development
Telemetry Analytics
Performance Benchmarking
Linux
Windows