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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 .
Embedded AI Tools Engineer
Wilmington MA
Analog Devices is uniquely positioned for success at the boundary of the physical and digital worlds. Analog Devices transforms physical phenomena – sound, light, radio waves, voltages, currents, and motion – into high fidelity data. Our mission is to build the Intelligent Edge where AI transforms the way we solve challenging problems by combining deep application knowledge, close customer relationships, extraordinary data, advanced circuits, and breakthrough algorithms.
Analog Devices has established the Embedded AI Tooling Team to provide development of next-generation embedded AI deployment infrastructure and model optimization tools for cutting-edge SoCs. Join our technically diverse team of AI infrastructure experts to unlock unprecedented AI performance in every domain.
Build Embedded AI Deployment Infrastructure for Intelligent Edge SoCs.
You will be a member of a core Advanced AI tooling team and will be instrumental in designing and implementing cutting-edge technology that will transform the semiconductor industry and enable embedded system engineers and research scientists to deliver AI-based solutions using ADI hardware.
During the first year, you will be part of a team responsible for achieving the following milestones:
- Work with team members to design, test, implement, and release novel AI model deployment tools and infrastructure for heterogeneous computer architectures such as DSPs, NPUs, and CPUs as part of a larger SoC.
- Build end-to-end workflows spanning model development, optimization, hardware-specific architecture, and deployment to ADI's embedded platforms—enabling developers to seamlessly move from model conception to production on resource-constrained devices.
- Develop tools and infrastructure for hardware-aware model design, including architecture mapping techniques that adapt neural network structures to leverage specific hardware capabilities (DSP operations, NPU features, mixed-precision computation).
- Design and implement model compilation and optimization pipelines that enable seamless quantization, pruning, layer fusion, and hardware-specific code generation for ADI's embedded hardware platforms.
- Explore and prototype agentic AI workflows that leverage autonomous agents for automated model-hardware co-optimization, intelligent architecture search, and adaptive deployment strategies.
Desired Skills and Experience:
- Strong embedded systems and computer architecture experience (bare-metal, RTOS, or embedded Linux).
- Expertise in end-to-end AI/ML model development, from training through optimization and deployment on embedded platforms.
- Experience with hardware-aware neural architecture design and model optimization techniques tailored to specific processor architectures.
- Proficiency in C, C++, Python, with experience in firmware and low-level software development.
- Deep understanding of neural network quantization, pruning, knowledge distillation, and optimization techniques for resource-constrained devices.
- Knowledge of neural network accelerators (NPUs, DSPs) and efficient execution of neural networks on heterogeneous hardware.
- Familiarity with AI/ML frameworks (TensorFlow, PyTorch) and deployment tools (TensorFlow Lite, ONNX Runtime, TVM, etc.).
- Experience with build systems (CMake, Make, Ninja), CI/CD pipelines, and infrastructure automation.
- Background in ML algorithms (CNN, DNN, Transformer architectures) and their embedded implementation.
- Familiarity with developer tooling (debuggers, profilers, SDKs, system configuration tools).
Bonus Points for:
- Experience with hardware-software co-design and custom operator development for specialized hardware.
- Knowledge of neural architecture search (NAS) and automated model optimization techniques.
- Background in digital signal processing (DSP) and algorithm implementation experience.
- Experience with edge AI frameworks and deployment tools (TensorFlow Lite Micro, ONNX, Apache TVM, MLIR).
- Understanding of compiler optimizations and code generation for embedded AI accelerators.
- Experience with FPGA development including design, synthesis, simulation, and verification.
- Experience with Zephyr RTOS and open-source RTOS ecosystems.
- Experience contributing to and working with open-source ecosystems.
- Understanding of heterogeneous architectures (ARM, RISC-V, DSPs, custom SoCs).
Why Join Us?
- Lead high-impact projects shaping the future of embedded AI deployment and hardware-aware model development.
- Work on cutting-edge embedded hardware with world-class teams.
- Be part of an innovation-driven environment, defining the next generation of AI deployment infrastructure for intelligent edge devices.
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 $121,600 to $167,200.
- 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 $121K-$167K range is below 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($144K) sits 32% below the category median. Disclosed range: $121K to $167K.
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
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