Interested in this AI/ML Engineer role at Analog Devices?
Apply Now →Skills & Technologies
About This Role
About Analog Devices
Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X .
Staff AI Platform Engineer, Developer Experience
Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at www.analog.com and on LinkedIn and Twitter (X).
This role is within the global Developer Experience ( DevEx ) team, specifically the Model Developer Experience (MDX) sub-team – whose mission is to deliver world-class infrastructure and platforms that enable AI builders across ADI to move faster, with confidence, and at scale. You will join a high-performance, mission-driven interdisciplinary team that spans data science, software engineering, platform architecture, cloud infrastructure, and security expertise . We believe in a culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people.
Role Summary
As a Staff/ Senior AI Platform Engineer (individual contributor), you will bring deep technical expertise in building scalable AI infrastructure across diverse deployment environments, and the ability to design elegant solutions that absorb operational complexity on behalf of AI builders across the organization. You will work embedded with data science and ML engineering teams to understand their infrastructure needs at the cutting edge , then translate those learnings into reusable, org-wide architectural patterns and platforms. You'll design and implement at critical infrastructure capabilities that span on-prem, hybrid, and cloud environments—from model serving and orchestration, to compute optimization, cost efficiency, and governance.
You will work at the intersection of research, technical architecture, and developer enablement—translating research and product goals into infrastructure systems that reduce friction and accelerate innovation.
Key Responsibilities
AI Developer Experience
- Partner directly with AI builder teams (data scientists, ML engineers, researchers) as an embedded technical advisor, understanding their infrastructure bottlenecks and platform needs at the point of creation.
- Translate specific team engagements into generalizable patterns, standards, and architectural guidance that can be adopted across the organization.
- Drive initiatives that reduce friction in the AI development lifecycle—from experimentation through production—by removing operational and technical barriers for builders.
- Design and advocate for developer-friendly abstractions and APIs that hide infrastructure complexity while maintaining flexibility for advanced use cases.
- Collaborate with cross-functional stakeholders to define what "excellent developer experience" means for AI developers .
- Shepherd ML/AI projects through ADI's AI Supply Chain ensuring governance gates are met at each transition
- Facilitate cross-functional approval workflows spanning Legal, Security, Trade Compliance, Data Privacy, and Responsible AI stakeholders for high-risk model promotions
On-Prem, Hybrid, and Cloud AI Infrastructure Engineering
- Design and optimize AI infrastructure strategies that span heterogeneous environments—on- premise GPU clusters, hybrid cloud-edge deployments, and cloud-native architectures—ensuring seamless developer experience across all.
- Architect compute orchestration and scheduling solutions (Kubernetes, Ray, or equivalent) that efficiently allocate resources across multiple environments and workload types.
- Own infrastructure for model serving, inference optimization, and real-time inference pipelines supporting low-latency, edge-deployed AI models.
- Define and implement cost optimization strategies across cloud and on-prem resources, including resource allocation, auto-scaling policies, and workload consolidation.
- Build reusable Infrastructure-as-Code frameworks and tooling that other teams can adopt to provision and manage AI workloads consistently across environments.
- Establish observability and monitoring strategies for AI infrastructure, including resource utilization , cost tracking, and performance metrics that enable proactive problem-solving.
- Drive security and compliance standards for AI infrastructure, ensuring data residency, access control, and auditability across deployment environments.
- Mentor engineers on infrastructure best practices, distributed systems concepts, and optimization techniques that improve platform reliability and developer productivity.
Required Skills & Experience
- Deep expertise in designing and operating AI workloads across multiple deployment paradigms (on-premises, hybrid, cloud-native).
- Proven ability to work embedded with technical teams, understand complex requirements, and translate them into scalable architectural solutions.
- Strong experience with Kubernetes, container orchestration, and distributed compute frameworks (Ray, Spark, or equivalent) at production scale.
- Expert-level Infrastructure-as-Code proficiency (Terraform, CDK, or equivalent) with demonstrated ability to build reusable, multi-team infrastructure templates.
- Deep knowledge of GPU/accelerator resource management, including scheduling, optimization, and cost tracking across heterogeneous hardware.
- Experience designing model serving infrastructure and inference optimization pipelines for production AI workloads.
- Demonstrated ability to solve complex infrastructure problems through systematic analysis and creative engineering.
- Strong communication skills and ability to translate technical concepts for both engineering and non-technical audiences.
- Familiarity with risk-tiered compliance models where control intensity scales with assessed risk level
- Strong cross-functional collaboration skills — ability to work across Legal, Security, Privacy, and Engineering to align on governance workflows without creating organizational friction
- Mentoring orientation with demonstrated success upskilling scientists on infrastructure and platform topics.
Preferred Skills – Physical Intelligence and Industrial AI
- Experience building or scaling AI infrastructure for robotics, autonomous systems, or industrial perception applications.
- Familiarity with ROS/ROS2 ecosystems and the infrastructure challenges of deploying ML models in robotic systems.
- Background in edge AI deployment, including optimization for low-latency inference on resource-constrained devices.
- Experience designing ML infrastructure that supports rapid iteration and few-shot adaptation in physical systems.
- Knowledge of heterogeneous compute architectures combining CPUs, GPUs, and specialized processors (NPUs, FPGAs, etc.).
- Experience with real-time operating systems or hard real-time constraints in distributed systems.
- Understanding of manufacturing, autonomous vehicle , or healthcare domains and their infrastructure requirements for AI applications.
For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.
Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.
EEO is the Law: Notice of Applicant Rights Under the Law .
Job Req Type: Experienced
Required Travel: Yes, 10% of the time
Shift Type: 1st Shift/Days The expected wage range for a new hire into this position is $172,000 to $236,500.
- Actual wage offered may vary depending on work location , experience, education, training, external market data, internal pay equity, or other bona fide factors.
- This position qualifies for a discretionary performance-based bonus which is based on personal and company factors.
- This position includes medical, vision and dental coverage, 401k, paid vacation, holidays, and sick time , and other benefits.
Salary Context
This $172K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Analog Devices, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $172K to $236K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Analog Devices AI Hiring
Analog Devices has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Wilmington, MA, US, Dallas, TX, San Jose, CA, US. Compensation range: $167K - $275K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.