below marketmost similar roles pay hereabove market
This role pays more than
92%
of similar roles. Most pay
$142,575–$215,325
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$179k
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.
The Senior Software Engineer joins the Product Analysis and Compliance Engineering organization to build intelligent systems at the intersection of AI, connected data, and regulatory compliance. You will design and ship AI-powered software systems that improve team efficiency by connecting disparate corporate data sources and automating decision-making. Key responsibilities include building robust data pipelines, integrating large language models with appropriate guardrails, and establishing continuous delivery pipelines optimized for DORA metrics. You will utilize Python, Kubernetes, and Docker to create production systems featuring comprehensive observability, secrets management, and audit trails for traceability. The role requires solving complex problems within a regulated environment, ensuring that products meet global safety and compliance standards through lean engineering practices, incremental delivery, and rigorous data classification for sensitive information.
What does a Software Engineer earn in California?
Median $214500 from 1193 postings across 76 companies.
Design, build, and ship AI-powered software systems to improve team efficiency through incremental delivery.
Integrate large language models into production systems with appropriate evaluation, guardrails, and monitoring.
Build and maintain robust data pipelines that connect corporate data sources while ensuring data quality and lineage.
Apply secure engineering practices including secrets management, data classification, and audit logging for sensitive data.
Establish and maintain continuous delivery pipelines optimized for DORA metrics like deployment frequency and lead time.
Build observability into every system using instrumentation, structured logging, alerting, and dashboards.
Ensure an audit trail exists for traceability and lineage of all AI and LLM-based decisions.
Write clean, testable, and well-factored code using continuous integration and small batch delivery.
What we're looking for
Bachelor's Degree in Computer Science, Computer Engineering, related field, or equivalent work experience.
7+ years experience building and shipping production software systems.
Strong track record of delivering AI-powered systems at scale, including model integration, evaluation, and production monitoring.
Deep practical experience with modern software engineering practices: continuous integration, continuous delivery, trunk-based development, and incremental delivery.
Proficient in Python and at least one other high-level programming language.
Experience building data pipelines and working with connected data across multiple sources.
Experience with cloud infrastructure and container technologies including Kubernetes and Docker.
Demonstrated ability to build observability into production systems including metrics, tracing, logging, and alerting.
Excellent written and verbal communication skills with both technical and non-technical audiences.
Master's degree in Computer Science, Computer Engineering, related field, or equivalent work experience (preferred).
Experience working in or building software for regulated industries (compliance, legal, safety, or similar domain) (preferred).
Familiarity with the principles in Accelerate and practical experience improving DORA metrics in a team setting (preferred).
Experience with test-driven development, continuous refactoring, small batch delivery, and collective code ownership (preferred).
Experience securing AI/LLM systems that process sensitive or regulated data (preferred).
Experience with LLM application patterns: retrieval-augmented generation, prompt engineering, evaluation frameworks, and human-in-the-loop workflows (preferred).
Experience with MLOps practices including model versioning, experiment tracking, and performance monitoring in production (preferred).
Experience helping establish engineering culture on a new or transforming team (preferred).
Python
Java
SQL
AWS
Kubernetes
CI/CD
AI Prompt Engineering
Agent-based Workflows
Databricks
Machine Learning
Large Language Models
DevOps
Data Modeling
System Design
Axiom ControllerView