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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 .
Principle Robotics Software Engineer (Physical AI)
The Dexterous AI Group (DAG) is working toward robots with human-level dexterity—the ability to manipulate objects with the versatility and adaptability people bring to everyday tasks. We combine tactile sensing with vision, robot learning, and control to advance that goal. We seek a hands-on Principal Robotics Software Engineer to lead software architecture across robot systems and the infrastructure used to develop, test, and deploy Physical AI. You will set technical direction and remain hands-on while guiding engineers with different experience levels to deliver shared technical milestones.
Why this role matters
You will own robotic manipulation use cases and define capabilities that demonstrate our solutions’ differentiation. You will lead the software and infrastructure supporting those capabilities across robot data collection and management, simulation and hardware experiments, model training, evaluation, and deployment. Shared tools and interfaces will let researchers compare ideas without rebuilding pipelines or robot integrations for each use case. Measurable success criteria and repeatable demonstrations will give customers and partners evidence of performance and integration requirements, while informing which capabilities merit further product investment.
Responsibilities
- Own manipulation use cases from definition through demonstration. Define capabilities and measurable success criteria, validate differentiation against agreed baselines, and set the platform architecture and integration priorities needed to deliver.
- Turn ambiguous R&D goals into executable plans. Identify unknowns, use focused experiments to reduce risk, and make and document technical trade-offs as evidence and priorities change.
- Lead delivery across engineers with varied experience. Break milestones into scoped work with clear ownership, dependencies, integration plans, and acceptance criteria; resolve blockers and communicate progress and risks early.
- Design, implement, and review core software in C++ and Python. Evolve the ROS 2-based stack and integrate hardware, sensing, control, simulation, and learned policies through maintainable interfaces.
- Bring production-grade engineering to research software. Establish appropriate automated tests, continuous integration, hardware-in-the-loop validation, observability, and release practices, with measurable reliability and performance targets.
- Partner with AI and other software engineers to design data collection and replay, dataset versioning, simulation, training and evaluation workflows, and deployment tooling. Connect these through shared data formats and reproducible environments across robot and remote compute.
- Align software decisions with hardware, controls, and AI constraints. Address timing, synchronization, bandwidth, failure handling, and safe robot operation; distinguish exploratory prototypes from capabilities ready for sustained use.
- Develop the team through design and code reviews, practical coaching, and clear technical documentation. Facilitate constructive disagreement, adapt communication to the audience, and help engineers deliver independently.
Required qualifications
- 8+ years developing software for robotics, autonomous systems, or embedded systems, including integration with physical hardware.
- Track record of delivering substantial software or systems from design into production, including integration, testing, debugging, maintenance, and reliability improvements.
- Strong C++ and Python skills, sound software architecture judgment, and hands-on experience with ROS 2 or comparable robotics middleware.
- Working knowledge of robot control, including feedback, timing constraints, actuator limits, and safe integration of learned policies with lower-level controllers.
- Working understanding of Physical AI infrastructure across data collection, simulation, training, evaluation, and deployment, with hands-on experience in part of that workflow and ability to assess reproducibility, data quality, compute needs, and latency constraints.
- Experience owning use cases: translating application needs into capabilities and success criteria, leading implementation and validation, and explaining demonstrated advantages, limitations, and integration requirements.
- Experience leading technical delivery without reporting-line authority, including scoping work, coordinating dependencies, mentoring engineers, and communicating decisions and risks across disciplines.
- Sound judgment in ambiguous R&D settings: validate assumptions, make decisions with incomplete information, and balance learning speed, maintainability, and delivery commitments.
- Bachelor’s degree in computer science, robotics, electrical or mechanical engineering, or a related field, or equivalent practical experience.
Preferred qualifications
The following are helpful but not required; expertise in every technical area is not expected.
- Robot manipulation, teleoperation, or integration of tactile, vision, and force sensing.
- Familiarity with robotic simulation tools such as Isaac Sim or MuJoCo.
- Deployment and evaluation of learned robot policies, including reinforcement learning, imitation learning, or vision-language-action models.
- ML data pipelines, GPU or distributed compute infrastructure, or edge inference.
- Controller design, such as impedance/force control or model-predictive control; real-time or embedded systems development.
- Interest in developing people-management skills over time.
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 $200,000 to $275,000.
- 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 $200K-$275K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Analog Devices, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($237K) sits 8% above the category median. Disclosed range: $200K to $275K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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