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Full Time Intel Physics Jobs (NOW HIRING)

Vescent Technologies

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FPGA - DSP Engineer

Golden, CO · On-site

$120K - $170K/yr

Golden (On-Site/Hybrid Desired, Fully Remote Possible) Job Type: Full Time Salary: $120,000 - $170 ... Required Qualifications · BS, MS, or PhD in Electrical Engineering, Physics, Applied Physics ...

Vescent Technologies

Be Seen First

FPGA - DSP Engineer

Golden, CO · On-site

$120K - $170K/yr

Golden (On-Site/Hybrid Desired, Fully Remote Possible) Job Type: Full Time Salary: $120,000 - $170 ... Required Qualifications · BS, MS, or PhD in Electrical Engineering, Physics, Applied Physics ...

Vescent Technologies

Be Seen First

FPGA - DSP Engineer

Golden, CO · On-site

$120K - $170K/yr

Golden (On-Site/Hybrid Desired, Fully Remote Possible) Job Type: Full Time Salary: $120,000 - $170 ... Required Qualifications · BS, MS, or PhD in Electrical Engineering, Physics, Applied Physics ...

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Full Time Intel Physics information

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How much do full time intel physics jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for full time intel physics in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Intel Physics professional, and why are they important?

To excel as a Full Time Intel Physics professional, you need a solid background in physics, mathematics, and data analysis, typically supported by at least a bachelor's or master's degree in physics or a related field. Familiarity with simulation software, programming languages like Python or MATLAB, and data visualization tools is often required. Strong analytical thinking, problem-solving skills, and effective communication are essential soft skills for interpreting complex data and collaborating with multidisciplinary teams. These competencies are vital to accurately analyze intelligence data, develop actionable insights, and support critical decision-making processes.

What is the difference between Full Time Intel Physics vs Full Time Data Analyst?

AspectFull Time Intel PhysicsFull Time Data Analyst
Required CredentialsPhysics degree, security clearance (often)Statistics, mathematics, or related degree
Work EnvironmentGovernment agencies, defense, intelligenceCorporate, finance, tech industries
Employer & Industry UsageMilitary, intelligence agenciesPrivate sector, consulting firms
Common Search & ComparisonYesNo

Full Time Intel Physics roles focus on applying physics knowledge within government and defense sectors, often requiring security clearances. In contrast, Full Time Data Analysts work primarily in the private sector analyzing data to inform business decisions. While both roles involve analytical skills, Intel Physics positions are more specialized in physics and security, whereas Data Analysts focus on data interpretation across industries.

What are some typical challenges faced by professionals in a full-time Intel Physics role, and how can they be addressed?

Professionals in a full-time Intel Physics role often encounter challenges such as staying current with rapidly evolving technologies and managing large datasets for analysis. Collaboration across multidisciplinary teams, including engineers and data scientists, is common and can require strong communication skills to ensure accurate interpretation of complex data. Addressing these challenges typically involves ongoing professional development, leveraging advanced analytical tools, and participating in regular team meetings to synchronize efforts and share insights.

What are Full Time Intel Physics jobs?

Full Time Intel Physics jobs typically involve conducting research and development in the field of physics, often within the context of technology and semiconductor companies like Intel. These roles may include experimental and theoretical work to design, test, and optimize physical processes and materials used in advanced computing hardware. Employees in these positions usually work on projects such as semiconductor device fabrication, quantum computing, and materials science. A full-time role means that the employee works a standard workweek, usually 40 hours, and receives full employment benefits.
More about Full Time Intel Physics jobs
What cities are hiring for Full Time Intel Physics jobs? Cities with the most Full Time Intel Physics job openings:
What are the most commonly searched types of Intel Physics jobs? The most popular types of Intel Physics jobs are:
What job categories do people searching Full Time Intel Physics jobs look for? The top searched job categories for Full Time Intel Physics jobs are:
Full Time Intel Physics jobs near you
Infographic showing various Full Time Intel Physics job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
Senior/Principal Lead Delivery Engineer - Semiconductor

Senior/Principal Lead Delivery Engineer - Semiconductor

Luminary Cloud

San Mateo, CA • On-site

$142K - $197K/yr

Full-time

Posted 25 days ago

Job description

Full-time position | San Mateo, CA (Onsite)
JOIN THE REVOLUTION IN ENGINEERING INNOVATION
Luminary helps engineering companies be more competitive by getting to market faster, creating better products, and reducing development risk. We do this through our Physics AI platform - the fastest and easiest way to build and deploy models that understand and instantly predict physical reality with precision. Our customers span industries from automotive and aerospace to defense, industrial, semiconductors, and energy - ranging from hyper-growth startups to Fortune 100 enterprises. Luminary is a Series B company headquartered in San Mateo, California.
YOUR IMPACT
As the Senior/Principal Lead Delivery Engineer for Semiconductor, you own customer outcomes for Luminary's engagements with semiconductor capital equipment makers, chip designers and foundries, and advanced packaging and photonics companies. You are a dual hat - project manager and product manager - leading a matrixed value delivery team across Applications Engineering, Applied AI/ML, Data & Platform, and product engineering. You translate the hardest problems in fab equipment design, chip thermal and electromagnetic analysis, and advanced packaging into Physics AI solutions, and partner with the Director of Industrial Sector on account engagement and growth.
WHAT YOU'LL DO
  • Own end-to-end customer outcomes across new and existing semiconductor accounts - from discovery and use case scoping through model development, deployment, and embedding into customer engineering workflows.
  • Author Statements of Work (SOWs), educate customers on the engagement plan, and own delivery accountability against them.
  • Lead a value delivery team in a matrix structure: Applications Engineers, Applied AI/ML Scientists, Data and Platform Engineers, and selected product engineers. Set technical direction, sequence the work, remove blockers.
  • Operate as a dual hat - project manager and product manager - running sprints, milestones, and risk while also choosing the right use cases and shaping the product intuition for what to build next.
  • Stay hands-on-keyboard where it matters. Debug a model, restructure a data pipeline, prototype a workflow integration. Lead from the front, not from slides.
  • Embrace co-engineering: work side-by-side with semiconductor engineers on use case development, sharing knowledge and building trust through visible, collaborative work.
  • Partner with the GTM team on new account engagement, GTM-to-delivery handoff, and account growth.
  • Bring market and customer signal back to Product and Research, directly shaping Luminary's roadmap for semiconductor use cases.

WHAT YOU BRING
  • 5-10 years of experience in a technical role in a relevant industry (engineering, simulation, R&D, advanced engineering, technical program management, or applied AI/ML). Principal-level candidates have an extensive track record in the semiconductor industry.
  • Multi-physics fluency across semiconductor disciplines: EDA, thermal and chip-package-board co-analysis, electromagnetics, signal and power integrity, fluid dynamics for fab equipment, structural mechanics, or multi-physics optimization. Generalist depth across several of these is more valuable than world-class depth in one.
  • Strong hands-on engineering chops - Python, scripting, simulation tools, and AI/ML workflows. Comfortable diving into code, models, or simulation setups when needed.
  • Working knowledge of AI/ML applied to engineering - surrogate modeling, neural operators, physics-informed ML, or digital twins.
  • Demonstrated ability to lead cross-functional technical teams without direct authority. You influence through clarity, credibility, and outcomes.
  • Product instinct: a track record of choosing the right problems to solve and shipping things that get used - not just demos that get cited.
  • Customer-facing presence: equally credible explaining a physics model to a chief engineer and walking a customer roadmap with Luminary leadership.
  • Self-starter mentality, comfortable with ambiguity, persistent through iteration. You question what you are asked to do and prioritize what actually moves the needle.
  • Willing to travel 10-25% to customer sites.

PREFERRED QUALIFICATIONS
  • Advanced degree (MS or PhD) in Electrical, Mechanical, Materials, or related engineering discipline.
  • Prior experience at semiconductor capital equipment companies (e.g., Applied Materials, Lam Research, KLA, ASML, Tokyo Electron), chip designers/foundries (e.g., NVIDIA, AMD, Intel, TSMC, Samsung, Qualcomm, Broadcom), or EDA/simulation companies serving semiconductor.
  • Experience with fab equipment engineering - etch, deposition, lithography, metrology - or chip-level thermal/EM analysis at advanced process nodes.
  • Familiarity with advanced packaging (2.5D/3D, chiplets, HBM) and the multi-physics challenges they introduce.
  • Track record of deploying ML or simulation tooling into production engineering workflows.

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