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About This Role
Location:
San Diego, CA, US
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pSemi Corporation is a Murata company driving semiconductor integration. pSemi builds on Peregrine Semiconductor’s 30\-year legacy of technology advancements and strong IP portfolio but with a new mission—to enhance Murata’s world\-class capabilities with high\-performance semiconductors. With a strong foundation in RF integration, pSemi’s product portfolio now spans power management, connected sensors, optical transceivers, antenna tuning and RF frontends. These intelligent and efficient semiconductors enable advanced modules for smartphones, base stations, personal computers, electric vehicles, data centers, IoT devices and healthcare. From headquarters in San Diego and offices around the world, pSemi’s team explores new ways to make electronics for the connected world smaller, thinner, faster and better.
Job Summary
As a Senior Staff Software Engineer focused on AI platforms, you will provide end\-to\-end technical leadership for the software systems that enable AI\-assisted RFIC design and verification workflows. Your primary mandate will be to define and build the platform through which AI\-enabled applications and intelligent agents safely interact with engineering tools, data, and computational infrastructure. You will architect scalable systems spanning user experiences, backend services, distributed workflows, engineering data, secure execution environments, and cloud or hybrid infrastructure.
This role is centered on systems engineering, platform architecture, and productionaization rather than foundational model research or model training. With significant autonomy, you will establish technical direction, convert emerging prototypes into dependable multi\-user products, and create reusable platform capabilities that can support multiple engineering applications. You will remain hands\-on while influencing architecture across teams, reducing systemic technical risk, and driving solutions from concept through deployment, operations, and continuous improvement. This is a senior individual contributor role for an expert engineer who can help build the software foundation for next\-generation semiconductor innovation, including 5G and beyond.
Roles \& Responsibilities
This position has responsibility for:
Own the architecture and evolution of distributed software platforms that support AI\-enabled engineering workflows. Define service boundaries, APIs, data flows, execution models, asynchronous processing patterns, and platform standards that can be reused across multiple products and teams. Establish a multi\-year technical direction while balancing near\-term delivery, maintainability, security, and operational risk.
Design and productionize systems that incorporate generative AI, LLM services, intelligent agents, retrieval, tool\-driven automation, and human approval workflows. Focus on reliability, evaluation, traceability, failure handling, policy enforcement, and secure execution rather than model research or training. Create common patterns that allow new AI capabilities and engineering tools to be integrated efficiently and safely.
Architect, implement, and maintain end\-to\-end software products spanning modern web applications, backend services, APIs, real\-time interactions, and engineering integrations. Establish scalable design patterns that enable teams to deliver intuitive, maintainable, and reliable user experiences while remaining hands\-on in critical areas of the codebase.
Design, automate, and continuously improve development, testing, deployment, and production environments across AWS, Microsoft Azure, Google Cloud Platform, on\-premises infrastructure, or hybrid environments. Establish robust practices for containerization, Kubernetes or comparable orchestration, infrastructure as code, CI/CD, configuration management, release automation, networking, storage, and environment promotion.
Lead the development of secure, resilient, and production\-ready systems with strong observability, fault tolerance, recovery, and operational support. Define standards for identity and access management, authorization, secrets and credential management, workload isolation, auditability, monitoring, incident response, root\-cause analysis, and service\-level objectives.
Collaborate across software, infrastructure, AI, and RFIC engineering teams to automate workflows involving design, simulation, testbench configuration, job execution, data collection, analysis, verification, and report generation. Integrate AI\-enabled capabilities with EDA tools and computational environments in ways that are safe, reproducible, traceable, and usable by engineers.
Direct the design of data architectures and integration patterns that connect applications, databases, engineering tools, AI services, enterprise systems, and long\-running computational workloads. Ensure data is reliable, secure, discoverable, and accessible, and establish resilient orchestration patterns for queued, asynchronous, and failure\-prone workflows.
Serve as a technical authority across multiple products and engineering disciplines. Lead architectural reviews, resolve complex cross\-system challenges, make build\-versus\-buy and platform\-standardization decisions, identify and retire systemic technical risk, establish engineering standards, and mentor other engineers. Drive alignment through technical judgment and influence rather than reporting authority.
Independently investigate emerging software, cloud, AI platform, infrastructure, and developer\-platform technologies. Identify practical opportunities to apply them within semiconductor engineering and produce clear architecture, design, operational, decision, and roadmap documentation that supports alignment, adoption, and long\-term maintainability.
Minimum Qualifications (Experience and Skills)
Typically requires 10\+ years of progressive, hands\-on software engineering experience, including technical leadership of complex, production\-scale systems and broad impact across multiple teams or product areas.
Deep proficiency in one or more modern programming languages, such as Python, Java, C\+\+, JavaScript, or TypeScript, with extensive experience in software architecture, debugging, automated testing, performance optimization, and maintainable code design.
Demonstrated expertise in designing, building, deploying, and operating distributed software platforms across multiple layers of the application stack, including user\-facing applications, APIs, backend services, data systems, workflow systems, and infrastructure.
Strong knowledge of distributed systems, API design, data modeling, asynchronous processing, messaging, system integration, fault tolerance, security, reliability, and production operations.
Experience deploying and operating production workloads in at least one major cloud platform, such as AWS, Microsoft Azure, or Google Cloud Platform, with practical knowledge of networking, compute, storage, identity, security, and managed services.
Experience with containerized applications, Kubernetes or comparable orchestration platforms, CI/CD, infrastructure as code, configuration management, environment promotion, monitoring, and incident response.
Experience designing secure multi\-user systems, including identity and access management, authorization, secrets and credential management, workload isolation, audit logs, and operational controls.
Experience integrating AI/ML services, LLM APIs, intelligent automation, or other nondeterministic components into production software systems. Expertise in training foundation models is not required.
Experience defining technical direction and leading cross\-functional teams through architecture, implementation, deployment, migration, and continuous improvement of complex products.
Exceptional written and verbal communication skills, with a proven ability to document and present technical designs, decisions, risks, tradeoffs, and roadmaps to technical and nontechnical stakeholders.
Track record of independent, high\-impact contributions that improve product capabilities, engineering velocity, system reliability, platform reuse, or organizational effectiveness.
Education Requirements
Bachelor’s degree in Computer Engineering, Computer Science, Electrical Engineering, or a related technical field. A master’s degree in Computer Engineering, Computer Science, Data Science, Electrical Engineering, or a related technical field is preferred.
Work Environment
This job operates in a professional office environment. This role routinely uses standard office equipment.
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee is regularly required to talk or hear. The employee frequently is required to stand; walk; use hands to finger, handle or feel; and reach with hands and arms. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception and ability to adjust focus. This position requires the ability to occasionally lift office products and supplies, up to 20 pounds.
USD 208,851\.58 \- 271,522\.09 per year
pSemi Corporation supports a diverse workforce and is committed to a policy of equal employment opportunity for applicants and employees. pSemi does not discriminate on the basis of age, race, color, religion (including religious dress and grooming practices), sex/gender (including pregnancy, childbirth, or related medical conditions or breastfeeding), gender identity, gender expression, genetic information, national origin (including language use restrictions and possession of a driver’s license issued under Vehicle Code section 12801\.9\), ancestry, physical or mental disability, legally\-protected medical condition, military or veteran status (including “protected veterans” under applicable affirmative action laws), marital status, sexual orientation, or any other basis protected by local, state or federal laws applicable to the Company. pSemi also prohibits discrimination based on the perception that an employee or applicant has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.
Note: The Peregrine Semiconductor name, Peregrine Semiconductor logo and UltraCMOS are registered trademarks and the pSemi name, pSemi logo, HaRP and DuNE are trademarks of pSemi Corporation in the U.S. and other countries. All other trademarks are the property of their respective companies. pSemi products are protected under one or more of the following U.S. Patents: http://patents.psemi.com
Additional Position Information:
Nearest Major Market: San Diego
Salary Context
This $208K-$271K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Murata America, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($240K) sits 10% above the category median. Disclosed range: $208K to $271K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Murata America AI Hiring
Murata America has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Diego, CA, US. Compensation range: $271K - $271K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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