Develop and optimize signoff methodologies for silicon design, focusing on PPA, reliability, and implementing automation with AI techniques.
Arm is shaping the next generation of silicon design by combining computing, AI, and multiphysics to redefine how future SoCs are designed, implemented, and verified. Our Signoff Methodology Team develops intelligent, automated environments for timing, IR, thermal, and reliability analysis across advanced process nodes and emerging 2.5D/3D SoC integration technologies.
This is a hands-on engineering role - you will write production-quality code, design automation flows, and integrate AI-driven techniques to advance the future of signoff methodology and intelligent silicon design.
We are currently hiring across three locations: San Jose, Austin, and San Diego
Responsibilities:
Required Skills and Experience :
"Nice To Have" Skills and Experience :
Our 10x mindset guides how we engineer, collaborate, and grow. Understand what it means and how to reflect 10x in your work: https://careers.arm.com/en/10x-mindset
Salary Range:
$241,100-$326,100 per year
We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm. Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.
Accommodations at Arm
At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email [email protected] . To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation or adjustment requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation. Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud, or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.
Hybrid Working at Arm
Arm's approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid working patterns, depending on the work and the team's needs. Details of what this means for each role will be shared upon application. In some cases, the flexibility we can offer is limited by local legal, regulatory, tax, or other considerations, and where this is the case, we will collaborate with you to find the best solution. Please talk to us to find out more about what this could look like for you.
Equal Opportunities at Arm
Arm is an equal opportunity employer, committed to providing an environment of mutual respect where equal opportunities are available to all applicants and colleagues. We are a diverse organization of dedicated and innovative individuals, and don't discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
This is a hands-on engineering role - you will write production-quality code, design automation flows, and integrate AI-driven techniques to advance the future of signoff methodology and intelligent silicon design.
We are currently hiring across three locations: San Jose, Austin, and San Diego
Responsibilities:
- Develop and code advanced signoff and implementation methodologies to optimize power, performance, area (PPA), yield, and time-to-market.
- Design and standardize Static Timing Analysis (STA) and signoff features used across all Arm silicon groups.
- Architect and automate signoff methodologies for STA, thermal, IR, and reliability - optimized for multiphysics accuracy, runtime efficiency, and scalability across large SoCs.
- Build and maintain automation frameworks integrating machine learning and data analytics for predictive design closure.
- Collaborate with Arm silicon design teams and EDA partners to standardize and scale signoff methodologies across multiple technology nodes.
- Develop data pipelines and visualization tools to monitor and optimize PPA, reliability, and yield metrics.
- Drive innovation in signoff methodology for advanced process technologies, ensuring readiness for new multiphysics effects - including 3DIC integration, thermal, dynamic IR, and reliability modeling.
Required Skills and Experience :
- 8-12 years of experience in SoC signoff or physical implementation methodology, including STA timing, reliability, and IR analysis, on advanced technology nodes.
- Strong programming and automation expertise in Python, TCL, or similar scripting languages.
- Deep understanding of timing closure, silicon correlation, and reliability modeling.
- Solid working knowledge of machine learning, statistical analysis, or AI-assisted flow development.
- Experience collaborating with EDA vendors on methodology development or feature enablement.
"Nice To Have" Skills and Experience :
- M.S. or Ph.D. in Electrical Engineering, Computer Engineering, or a related field.
- Familiarity with innovative AI, including GenAI and LLM-based tools, for data-centric design automation.
- Strong problem-solving, algorithmic, and analytical thinking, with attention to scalability and reproducibility.
- Effective communication and technical leadership across multi-functional teams.
Our 10x mindset guides how we engineer, collaborate, and grow. Understand what it means and how to reflect 10x in your work: https://careers.arm.com/en/10x-mindset
Salary Range:
$241,100-$326,100 per year
We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm. Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.
Accommodations at Arm
At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email [email protected] . To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation or adjustment requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation. Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud, or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.
Hybrid Working at Arm
Arm's approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid working patterns, depending on the work and the team's needs. Details of what this means for each role will be shared upon application. In some cases, the flexibility we can offer is limited by local legal, regulatory, tax, or other considerations, and where this is the case, we will collaborate with you to find the best solution. Please talk to us to find out more about what this could look like for you.
Equal Opportunities at Arm
Arm is an equal opportunity employer, committed to providing an environment of mutual respect where equal opportunities are available to all applicants and colleagues. We are a diverse organization of dedicated and innovative individuals, and don't discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Top Skills
AI
Automation Frameworks
Data Analytics
Machine Learning
Python
Tcl
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