Reinforcement Learning From Human Feedback Engineer 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

Join a team building LLM-powered systems that improve personalization, intelligent automation, and customer understanding across Apple’s services ecosystem. This Senior Machine Learning Engineer role sits within Apple Services Engineering and combines hands-on generative AI development with research-to-production impact, with opportunities to influence how advanced NLP systems behave in real user experiences.

Location: Cupertino, CA (onsite)

Pay: USD 184,700 - 324,800 per year

What you’ll do

Architect, design, and deploy LLM-powered systems enabling new capabilities for personalization, intelligent automation, and customer understanding across Apple services.
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 approaches such as parameter-efficient adaptation, multi-agent orchestration, active learning, RLHF, and novel inference optimization techniques.
Partner with engineering, product, and design organizations to translate ambiguous problem spaces into ML systems with measurable business and customer impact.
Build prototypes and production-grade solutions that help Apple reason over text, behavioral signals, and domain-specific knowledge at scale.
Support Apple’s AI leadership through patent filings, publications, and internal thought leadership.
Mentor other researchers and raise standards for experimentation, code quality, communication, and scientific rigor.

What you bring

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 experience, 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 designing and developing RL or multi-armed bandit experiment platforms.
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 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, with experience deploying models in production systems.
Strong understanding of distributed data processing systems (e.g., Spark) and large-scale experimentation.
Ability to communicate research results, architecture decisions, and technical tradeoffs to technical and non-technical stakeholders.

Technologies you’ll work with

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

Benefits

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)
Discretionary restricted stock unit awards
Employee Stock Purchase Plan (purchase Apple stock at a discount if voluntarily participating)
Discretionary bonuses or commission payments and relocation (as applicable)

Pay & stock opportunity

The base pay range for this role is between $184,700 and $324,800. Apple employees may be able to participate in Apple’s discretionary employee stock programs and Employee Stock Purchase Plan. This role might be eligible for discretionary bonuses or commission payments as well as relocation (as applicable).

View full job page →

Get Reinforcement Learning From Human Feedback Engineer Jobs alerts

New Reinforcement Learning From Human Feedback Engineer Jobs, daily.