Reinforcement Learning From Human Feedback Data Jobs

1 job found

Today

Senior Machine Learning Engineer

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $325k/yr Full time Agentic Ai Ai Agent Ai Ml

Apply Now

Job Description

Apple Services Engineering is hiring a Senior Machine Learning Engineer to build and lead LLM-powered systems that support personalization, intelligent automation, and a deeper understanding of customers across Apple’s services ecosystem. The role spans the full lifecycle from research inception to production deployment, with a focus on advanced NLP and practical Generative AI engineering.

What you’ll do

Architect, design, and deploy LLM-powered systems that enable new capabilities for personalization, intelligent automation, and customer understanding across Apple’s services ecosystem.
Lead research in large-scale representation learning, semantic modeling, topic induction, natural language understanding, and retrieval-augmented generation (RAG).
Develop and evolve taxonomies, embeddings, and model architectures to disentangle complex user or content behaviors in high-dimensional, unstructured data.
Drive LLM fine-tuning, evaluation, safety alignment, and optimization strategies to support performant, compliant, and frictionless user experiences.
Explore and productize techniques including parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF, and inference optimization approaches.
Partner with engineering, product, and design teams to convert ambiguous problem spaces into robust ML systems with measurable business and customer impact.
Build prototypes and production-grade solutions that improve scale reasoning over text, behavioral signals, and domain-specific knowledge.
Support Apple’s AI leadership through patent filings, publications, and internal thought leadership.
Mentor and elevate other researchers by strengthening experimentation, code quality, communication, and scientific rigor.

Required qualifications

Hands-on experience with retrieval-augmented generation (RAG) pipelines and vector-based semantic search systems.
Representation learning and semantic embeddings for clustering, categorization, and content understanding.
Model evaluation frameworks for language quality, relevance, hallucination, and safety.
Inference optimization techniques including quantization, distillation, and model compression.
Understanding of reinforcement learning, policy alignment, or RLHF for improving interactive AI systems.
Experience developing personalization, ranking, or optimization algorithms at scale.
Proven experience architecting and developing an RL or multi-armed bandit experiment platform.
A record of publications in top-tier ML/AI venues or patent filings showing novel research contributions.
Ph.D. in Computer Science, Machine Learning, NLP, Statistics, or a related field, or equivalent industry experience delivering production AI systems.
At least 6 years of experience in an applied research or machine learning role.
Expert knowledge of deep learning and modern NLP, including transformer architectures and foundation model adaptation.
Experience with LLM model development, including fine-tuning, instruction tuning, and prompt engineering for domain-specific reasoning.
Proficiency in Python and ML frameworks such as PyTorch or TensorFlow, including experience deploying models in production systems.
Strong understanding of distributed data processing systems such as Spark and large-scale experimentation.
Ability to communicate research outcomes, architectural decisions, and technical tradeoffs to technical and non-technical stakeholders.

Technologies

LLM-powered systems, Generative AI, LLM architectures, advanced NLP systems
Retrieval-augmented generation (RAG), vector-based semantic search systems, embeddings
Python, PyTorch, TensorFlow, Spark
Transformer architectures, foundation model adaptation
Fine-tuning, instruction tuning, prompt engineering
Parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF
Quantization, distillation, model compression
Reinforcement learning, policy alignment, multi-armed bandit

Location

Cupertino, CA (onsite)

Compensation and benefits

Base pay range: $184,700 - $324,800 per year.
Comprehensive medical and dental coverage.
Retirement benefits.
Discounted products and free services.
Reimbursement for certain educational expenses, including tuition.
Discretionary employee stock programs (eligibility requirements apply), including discretionary restricted stock unit awards.
Employee Stock Purchase Plan (purchase Apple stock at a discount, if voluntarily participating).
Discretionary bonuses or commission payments as well as relocation (as applicable).

Apple employees may also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs and can purchase Apple stock at a discount through the Employee Stock Purchase Plan, if voluntarily participating.

View full job page →

Get Reinforcement Learning From Human Feedback Data Jobs alerts

New Reinforcement Learning From Human Feedback Data Jobs, daily.