below marketmost similar roles pay hereabove market
This role pays less than
77%
of similar roles. Most pay
$168,475–$241,750
— the blue band above.
At the midpoint, this role pays about
$162k
versus about
$205k
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
3595 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2772 roles with salary data.
The Machine Learning Engineer - Agentic AI joins the VCV organization to develop and integrate large language models and cutting-edge AI techniques into agentic systems. This role involves designing and building end-to-end systems that combine models, tools, and data systems to execute multi-step workflows in production environments. Key responsibilities include designing system behaviors for planning, tool use, structured outputs, and failure handling while balancing tradeoffs between latency, cost, accuracy, and controllability. You will develop infrastructure for evaluating performance, reliability, and cost, and build monitoring systems to observe behavior. The role requires proficiency with LLM-assisted coding and experience with agentic systems. The work focuses on enhancing the autonomy and intelligence of systems by integrating LLMs with internal and external tools to retrieve context and execute actions.
What does a Machine Learning Engineer earn in California?
Median $231150 from 191 postings across 27 companies.
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
Large Language Models (LLMs)
Large Multimodal Models (LMMs)
PyTorch
TensorFlow
JAX
Transformers
Mixture-of-Experts (MoE)
Distributed Data Processing
Computer Vision
Natural Language Processing
Synthetic Data Generation
Production-scale Inference
Data Curation
High Performance Computing
Python
C++
PyTorch
CUDA
GPU Kernel Optimization
Distributed Training
Data Pipelines
Inference Serving
Computer Architecture
Model Deployment
Data Ingestion
Profiling
Debugging
Claude
Python
C++
PyTorch
CUDA
GPU Kernel Optimization
Distributed Training
Data Pipelines
Inference Serving
Computer Architecture
Model Deployment
Data Ingestion
Profiling
Debugging
Claude