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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 is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. Apple’s Sales Engineering team is shaping the future of Channel Sales with innovative, high\-impact applications. We’re looking for a Machine Learning Engineer to help us design and build the next generation of intelligent systems that power Apple’s global partner ecosystem. In this role, you’ll develop and deploy machine learning solutions while leveraging generative AI and advanced ML capabilities to deliver scalable, production\-ready systems that accelerate strategic, high\-impact initiatives across Apple Channel Sales. 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
Apple’s Sales Engineering Rapid Application Development (RAD) team is looking for a Machine Learning Engineer to build intelligent, scalable solutions that power Apple’s global Channel Sales. You’ll leverage generative AI and advanced machine learning technologies to deliver high\-performance, production\-ready systems that drive measurable business impact. The ideal candidate blends deep ML expertise with strong engineering skills, is passionate about applying AI to solve real\-world problems, and thrives in fast\-paced environments delivering value quickly. You’ll work side by side with product, design, and engineering teams to design, train, deploy, and optimize ML\-powered applications that push the boundaries of innovation\-whether enabling GenAI\-driven workflows, implementing RAG\-based systems, or pioneering new intelligent capabilities. If you’re excited about shaping impactful AI solutions in a collaborative, experiment\-driven environment, Sales Engineering RAD team is where you’ll thrive.","responsibilities":"Design, build, and deploy scalable machine learning and generative AI solutions that power Apple’s global Channel Sales ecosystem.
Develop and optimize ML pipelines leveraging LLMs, LMMs, and RAG\-based architectures for production\-grade applications.
Collaborate with cross\-functional teams to translate business needs into intelligent, data\-driven systems and workflows.
Fine\-tune and evaluate transformer\-based models (e.g., GPT, LLaMA, BERT) for accuracy, performance, and scalability.
Prototype and productionize emerging AI capabilities, including agentic workflows and generative assistants.
Apply MLOps best practices for model training, deployment, monitoring, and continuous improvement.
Ensure secure, compliant handling of sensitive data (including PII) while maintaining Apple’s privacy standards.
Preferred Qualifications
Proven ability to fine\-tune, adapt, and deploy LLMs/LMMs into real\-world, production\-grade applications.
Proficiency in Python and leading ML frameworks such as PyTorch and TensorFlow.
Hands\-on experience leveraging Hugging Face Transformers and associated libraries.
Solid understanding of Retrieval\-Augmented Generation (RAG) and practical experience with orchestration frameworks like LangChain or LlamaIndex.
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.
Experience extending beyond traditional LLMs/LMMs to include agent\-based systems and agentic workflows.
Proficiency with advanced LLM serving and inference frameworks, ensuring scalable and efficient model deployment.
Practical experience building sophisticated RAG applications and orchestrating complex LLM pipelines from inception to deployment.
Working knowledge of distributed systems and cloud\-native infrastructure.
Expertise in optimizing transformer\-based architectures (e.g., BERT, GPT, LLaMA) for low\-latency, high\-performance inference.
Demonstrated ability to communicate complex technical results and ML/LLM concepts with clarity and impact to both technical and non\-technical stakeholders.
Experience applying ML methodologies in specific domains, such as sales.
Minimum Qualifications
M.S. in Computer Science, Machine Learning, Artificial Intelligence, or a closely related technical field, or equivalent practical experience.
5\+ years experience developing and deploying machine learning solutions, with a strong focus on Large Language Models (LLMs) or Large Multimodal Models (LMMs).
5\+ years experience with LLMs and transformer\-based architectures (e.g., BERT, GPT, LLaMA).
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Apple, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Apple AI Hiring
Apple has 160 open AI roles right now. They're hiring across Research Engineer, MLOps Engineer, AI/ML Engineer, AI Software Engineer. Positions span Cupertino, CA, US, Austin, TX, US, Santa Clara, CA, US. Compensation range: $153K - $487K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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