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About This Role
AI Software Staff Engineer
Location: Campbell, CA
Arteris connects innovation.
Our technology helps the world’s most visionary companies—from startups to Fortune 500 leaders—build smarter, faster semiconductors, specifically SoCs and chiplets. From the car you drive, to the AI in the cloud, Arteris connects the innovative tech that shapes tomorrow.
What You’ll Do as aAI Siftware Staff Engineer at Arteris
Join our innovative team and help shape the future of semiconductor technology, specifically system-on-chip (SoC) and chiplets. In this role, you will contribute to [insert a high-level purpose or function of the role], collaborating with cross-functional teams to drive impact across cutting-edge products and solutions.
You will work at the intersection of software engineering, AI application development, and deployment infrastructure, within a technology environment focused on EDA, security, semiconductors, and SoC design.
You will collaborate with our research and development team who develop cutting edge applications of AI towards security solutions, with the objective of transforming these solutions into robust, reliable, production-ready applications and services.
This is not primarily an AI research or model-development role. We are looking for a Software Engineer with a strong understanding of AI technologies who can build the systems required to bring them into production.
You will work with applications based on LLMs, chatbots, RAG systems, AI assistants, and AI agents, while addressing the technical challenges involved in their application, integration and deployment.
Key Responsibilities
- Turn AI/ML and LLM proof-of-concepts into production-grade software applications and services. Design, develop, and maintain software components surrounding AI models, including APIs, backend services, data pipelines, integrations, and user-facing applications.
- Develop and deploy LLM-powered applications, including chatbots, RAG systems, AI assistants, and agent-based applications.
- Integrate existing AI/ML models and services into broader software products and workflows.
- Deploy AI applications in cloud and/or on-premises environments, ensuring scalability, reliability, performance, and maintainability.
- Work with containerized and distributed environments and contribute to deployment automation and CI/CD pipelines.
- Address production engineering requirements including security, networking, authentication, authorization, monitoring, logging, observability, and resource management.
- Collaborate with Software, Infrastructure, Hardware, and Application teams to integrate AI capabilities into existing EDA, security, semiconductor, and SoC design workflows.
- Investigate and resolve production issues related to AI services, applications, deployment, performance, and integration.
- Contribute to software architecture and make pragmatic technical decisions that balance development speed, reliability, scalability, and maintainability.
- Write high-quality, maintainable, well-tested production code and contribute to engineering best practices.
What You Bring
- 5+ years of professional software engineering experience, with strong programming and software development skills.
- Strong experience with technologies such as LangGraph, Docker, Kubernetes, CI/CD, REST/gRPC APIs, databases, monitoring, and logging.
- Strong experience with Python, C++, or similar programming languages.
- Experience using AI/ML models as part of software applications.
- Experience developing production-quality software, APIs, backend services, or distributed applications.
- Experience deploying software in cloud or on-premises environments.
- Understanding of software security concepts, networking, authentication, authorization, and production deployment practices.
- Good understanding of software architecture, data structures, algorithms, and object-oriented design.
- Ability to understand and work within complex technical domains, including hardware and semiconductor technologies.
- Ability to collaborate effectively with AI/ML specialists and transform experimental prototypes into robust software solutions.
Bonus Points If You Have
- Experience bringing an AI or LLM proof-of-concept into production.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with LLM frameworks, vector databases, RAG architectures, or agentic systems.
- Experience with semiconductor IP, interconnects, or Network-on-Chip (NoC) technologies.
- Previous experience in EDA or semiconductor environments is a strong plus.
- Experience or background in hardware security.
Education Requirements
- Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Artificial Intelligence, or a related technical field required.
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline preferred.
Estimated Base Salary
- $165,000 to $190,000 annually. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
About Arteris
Arteris is a global leader in system IP used in semiconductors to accelerate the creation of high-performance, power-efficient silicon. Arteris network-on-chip (NoC) interconnect IP system-on-chip (SoC) integration automation software and hardware security assurance technology are used by the world's top semiconductor and technology companies to improve overall performance, engineering productivity, reduce risk, lower costs, and bring complex designs to market faster. Learn more at arteris.com.
With over 380 team members headquartered in Silicon Valley and offices around the world, we work with startups and global tech leaders alike to build the next generation of electronic products. We believe in people, purpose and impact. Join us and help shape what comes next.
Salary Context
This $165K-$190K 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 ARTERIS, 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. This role's midpoint ($177K) sits 16% below the category median. Disclosed range: $165K to $190K.
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.
ARTERIS AI Hiring
ARTERIS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Campbell, CA, US. Compensation range: $190K - $190K.
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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