Interested in this Research Engineer role at Apple?
Apply Now →Skills & Technologies
About This Role
The Apple Services Engineering (ASE) team is one of the most exciting examples of Apple’s long\-held passion for combining art and technology. People here create the experiences loved by users across App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. The scale is massive delivering content and entertainment in over 35 languages to more than 150 countries, while meeting Apple’s high bar on quality and performance. The team is responsible for building secure, robust, end\-to\-end solutions across server and client to solve challenging problems. Thanks to Apple’s unique integration of hardware, software, and services, engineers here work with a single unified vision of deep commitment to strengthening Apple’s core principles such as customer focus, privacy, and relentless innovation. Although services are a bigger part of Apple’s business than ever before, these teams remain small, nimble, and cross\-functional, offering an opportunity to work with passionate people, contribute ideas, and ship innovative software. Here, you’ll do more than just join a team \- you’ll be creating positive impact in people’s lives.
Description
The ASE Search team is a vital part of Apple ecosystem, powering search for App Store, Apple Music, Apple TV, Podcasts, Books, iTunes and more, on a wide set of platforms such as iOS, macOS, tvOS, watchOS, Safari, and 3rd party devices. Driven by passion for the extraordinary rather than the easy, our team of problem solvers, is dedicated to helping users discover media and content in exciting new ways. We are looking for extraordinary and motivated machine learning researchers and engineers to join us in our journey. As a Senior/Staff Machine Learning Research on the ASE Search team, you will lead the design and development of next\-generation search and conversational discovery features for Apple's ground breaking devices and platforms.","responsibilities":"Build next\-gen experiences influencing the way people search \& discover on Apple devices worldwide.
Drive innovation by staying at the forefront of research and applying it to complex problems with rigor and principled thinking.
Architect foundational systems across multiple surface areas \- retrieval, ranking, indexing, query and document understanding, and agentic/RAG\-based capabilities.
Define and drive the technical roadmap for search ML capabilities, aligning short\-term execution with long\-term strategic goals.
Partner with engineers, researchers, product, and platform teams to identify high\-impact opportunities and deliver robust solutions.
Own the ML lifecycle end\-to\-end \- from formulating objectives and defining data and training strategies through production integration.
Build components in large\-scale distributed backend systems using high\-performance programming languages such as Go, Java, Python, and Scala.
Present key technical and novel research work in public forums.
Design A/B experiments with well\-defined KPIs to objectively measure improvements.
Design testing, monitoring, and alerting solutions to maintain high operational SLAs for production systems.
Mentor engineers and raise the bar on ML best practices, code quality, and system design.
Preferred Qualifications
MS or Ph.D. in Computer Science or related subject area
Proven ability to build \& scale Search \& Conversational systems, applying 7\+ years of hand\-on experience across the full product stack \- including query understanding, semantic retrieval, multi\-stage ranking, indexing, intent classification, and context\-aware generation.
Deep expertise in Search \& Conversational systems, bringing in 7\+ years of hands\-on experience building capabilities such as query understanding, retrieval, ranking, indexing, autocomplete, intent resolution, and context\-aware generation across multiple domains.
Proficient in developing robust big data pipelines in Scala or Python using distributed processing frameworks like Apache Spark.
Familiarity with scalable, reliable distributed backend services including Kubernetes, cloud infrastructure, and container orchestration
Familiarity with A/B experimentation and data\-driven product development
Minimum Qualifications
3\+ years of relevant industry experience building large\-scale ML \& data systems
Familiarity with search or recommendation systems, conversational engines, or related domains
Strong knowledge of generative AI systems including Large Language Models, Transformers, Reinforcement Learning, RAG, and agentic patterns such as ReAct, Chain\-of\-Thought, Tool Use, and Multi\-Agent orchestration
Experience with one or more distributed ML training frameworks such as PyTorch, TensorFlow, Ray, or JAX, and inference engines like TensorRT or vLLM
Technical leader with exceptional communication skills and a track record of solving complex, ambiguous problems in a highly collaborative environment
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 $175,000 and $308,500, 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 $175K-$308K range is above the 75th percentile for Research Engineer roles in our dataset (median: $188K across 44 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Apple, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $280,000 based on 147 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($241K) sits 14% below the category median. Disclosed range: $175K to $308K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Apple AI Hiring
Apple has 28 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Santa Clara, CA, US, Cupertino, CA, US, Seattle, WA, US. Compensation range: $225K - $381K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.