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About This Role
Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Apple’s Sales Engineering team is shaping the future of Channel Sales with innovative, high\-impact applications. We’re looking for a Senior Software Engineer to help us design and build the next generation of intelligent systems that power Apple’s global partner ecosystem.In this role you will be partnering with cross functional teams across Apple. You will work closely with our business leaders and other partners to implement these new solutions. The candidate must be able to work independently, understand the needs and build the solutions for sophisticated architecture and comfortable working under pressure at times.If you’re passionate about applying AI to solve complex business problems, experimenting with emerging GenAI technologies, and building products that make a real difference, join our collaborative team and help us move fast on game\-changing ideas.
Description
Join Apple's Sales Engineering team as a Senior Software Engineer and help us build the intelligent, scalable solutions that drive Apple's global Channel Sales! You'll be at the forefront, architecting, designing, and implementing highly available enterprise solutions that truly match Apple's scale. We're looking for someone who blends deep expertise in backend technologies with strong engineering skills. You're passionate about applying design patterns to solve real\-world problems and thrive in a fast\-paced environment where delivering value quickly is key. You'll collaborate closely with product, design, and engineering teams to design, develop, deploy, and optimize ML\-powered applications that push the boundaries of innovation. You'll have the opportunity to choose the right technology stack, combining relevant SQL and NoSQL technologies to architect optimal solutions. Leverage your expertise in Java frameworks including Spring, REST, etc. You will also drive technical design and perform code reviews. If you're excited about shaping impactful solutions in a collaborative, experiment\-driven environment, the Sales Engineering team is the place to be!
","responsibilities":"Solid understanding of Machine Learning, Deep Learning (including LLMs) and Natural Language Processing.
In\-depth knowledge of technologies such as Web Services, XML, JSON, HTTP, SSL, TCP/IP, Caching solutions, application performance tuning.
Significant experience developing, orchestrating and maintaining high volume web applications, developing secure web applications, and building and managing RESTful services.
Well versed with the use of XML and JSON.
Experience in designing and handling systems with high performance, scalability and availability.
Ability to work in a collaborative team environment on fast\-moving projects.
Ability to successfully multi\-task and support multiple, concurrent projects.
Familiarity with distributed computing, cloud platforms (AWS, GCP, Azure), and containerization/orchestration tools (Docker, Kubernetes).
Exceptional problem\-solving skills and the ability to articulate complex ML/AI concepts clearly and effectively to diverse audiences.
Preferred Qualifications
10\+ years Java \& distributed databases.
5\+ years design, development and deployment of enterprise systems.
Experience with Apache Kafka and Apache Solr is a plus.
Experience with big data pipelines using Hadoop and Apache Spark is a plus.
Experience with leading and mentoring Engineers.
Strong curiosity and a willingness to learn and talk about emerging technologies along with a positive and enthusiastic engagement style.
Experience extending beyond traditional LLMs/LMMs to include agent\-based systems and agentic workflows.
Good understanding of various distributed system concepts.
Experience in designing and building Active\-Active systems.
Experience with Java and J2EE, Java11 related technologies: Spring Framework, spring\-boot, JUnit / TestNG. Strong knowledge and understanding of data structures, algorithms, design patterns, concurrency, multi\-threading, scalability, fault tolerant designs, enterprise architecture and software engineering principles.
Solid understanding of data modeling and database systems including Oracle, Cassandra, other NOSQL technologies.
Minimum Qualifications
Bachelors Degree in Engineering, or equivalent experience.
10\+ years leading software design \& architecture.
10\+ years large scale distributed systems.
Hands on experience with GenAi technologies.
Pay \& Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $212,000 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses \- including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Salary Context
This $212K-$318K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $186K across 153 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 2,799 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Apple, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $240,000 based on 600 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,500. This role's midpoint ($265K) sits 10% above the category median. Disclosed range: $212K to $318K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Apple AI Hiring
Apple has 102 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, MLOps Engineer. Positions span Austin, TX, US, Sunnyvale, CA, US, Cupertino, CA, US. Compensation range: $207K - $487K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (98) are outnumbered by mid-level (1,283) and senior (1,092) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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