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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. The G&A Solutions Engineering organization at Apple primarily focuses on creative ways to engineer business solutions to meet growing needs of Apple's Finance, i Tunes, Sales, Retail, and Services organizations. At core, our portfolio comprises of engineered custom solutions to process high volume transactions from Apple Pay, i Tunes, Ads, App Store, i Phone Activations to Sales from Retail, Online, and Resellers. These solutions are based on cutting edge enterprise technologies ranging from Distributed Systems, Microservices, Java, Spring/Boot, Oracle, MongoDB, AWS services to AI/ML, Generative AI, and Blockchain. Accurately processing such high volume transactions is our core strength.
Description:
The i Recon Payments team is seeking a highly motivated AI/ML Engineer to help build our next-generation payments platform. In this role, you will blend classical ML with cutting-edge Generative and Agentic AI to transform how we process transactional data at scale.
","responsibilities":"You will act as a technical catalyst, modernizing complex product architectures to enable full observability and autonomous workflows for reconciliation, invoicing, and payments
We are looking for a self-starter who can navigate the intersection of financial data and Large Language Models to drive productivity and operational efficiency
Preferred Qualifications:
3+ years deploying production-grade AI/ML solutions in the Fin Tech domain
2+ years building conversational assistants or autonomous agents using advanced techniques (Lang Graph, CrewAI, A2A, CoT, Re Act, Reflection)
Experience with the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques (RLHF, PPO, GRPO)
Demonstrated ability to quickly master emerging AI tools and integrate them into legacy stacks
Strong written and verbal communication skills with the ability to explain complex AI concepts to business stakeholders
Minimum Qualifications:
2+ years of experience building machine learning solutions using supervised/unsupervised learning, classification, recommendation systems, and clustering algorithms
In-depth knowledge of transformer architecture, LLMs, and Agentic AI concepts
Hands-on experience fine-tuning Large Language Models (LLMs) using PEFT/LoRA for domain-specific tasks
Proven experience building and extending RAG, MCP (Model Context Protocol), or multi-agent frameworks (e.g., Lang Chain, Llama Index, Auto Gen)
Bachelor's degree in Computer Science, AI, Machine Learning, or relevant work experience
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 .
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About Apple

Apple
PublicA technology company that designs, manufactures, and markets consumer electronics, personal computers, and software.
10,001+
Employees
Cupertino
Headquarters
$3.5T
Valuation
Reviews
4.0
10 reviews
Work Life Balance
4.0
Compensation
4.2
Culture
3.8
Career
3.5
Management
3.2
75%
Recommend to a Friend
Pros
Great coworkers and people
Excellent benefits and perks
Fast-paced and engaging work environment
Cons
High expectations and pressure
Management quality varies
Limited career progression opportunities
Salary Ranges
17,968 data points
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Principal/L7
Senior/L5
Staff/L6
Junior/L3 · Data Scientist ICT2
0 reports
$121,979
total / year
Base
-
Stock
-
Bonus
-
$103,682
$140,276
Interview Experience
5 interviews
Difficulty
3.4
/ 5
Duration
28-42 weeks
Offer Rate
20%
Experience
Positive 20%
Neutral 40%
Negative 40%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Behavioral Interview
5
Onsite/Virtual Interviews
6
Team Matching
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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