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Nvidia Machine Learning Jobs in Texas (NOW HIRING)

Nvidia

Senior AI and ML HPC Cluster Engineer

Austin, TX

$103K - $142K/yr

NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life's work, to ...

Nvidia Corporation

Senior Compiler Engineer - AI

Austin, TX · On-site

$121K - $160K/yr

The ideal candidate brings broad experience across machine learning, including reinforcement ... NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work ...

Nvidia

Senior Developer Technology Engineer - AI

Austin, TX · Hybrid

$54 - $71.25/hr

... in deep learning, machine learning or other AI domains. * Work directly with other technical ... NVIDIA's success in the advancement and availability of Artificial Intelligence has created ...

Nvidia

Principal SoC Architect - Hardware

Austin, TX

NVIDIA is developing processor and system architectures that are at the forefront of accelerating machine learning, automotive and high-performance computing applications. We are building the most ...

Nvidia

Datacenter GPU Power Architect

Austin, TX

You will help with Performance vs Power Analysis, track ASIC milestones for impactful NVIDIA future product lineup. * Deploy machine learning techniques to develop highly accurate power and ...

Nvidia

Senior Hardware SoC Architect

Austin, TX

NVIDIA is developing processor and system architectures that are at the forefront of accelerating machine learning, automotive and high-performance computing applications. We are building the most ...

Nvidia

Senior Datacenter GPU Power Architect

Austin, TX · On-site

$66.75 - $89.25/hr

Deploy machine learning techniques to develop highly accurate power and performance models of our ... NVIDIA is widely considered to be one of the technology world's most desirable employers. Our ...

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Nvidia Machine Learning information

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$23.8K

$39.7K

$82K

How much do nvidia machine learning jobs pay per year?

As of Jun 9, 2026, the average yearly pay for nvidia machine learning in Texas is $39,673.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,300.00 and $42,900.00 per year, depending on experience, location, and employer.

What is a Nvidia Machine Learning job?

A Nvidia Machine Learning job involves developing and optimizing AI models, deep learning frameworks, and GPU-accelerated applications. Engineers in this role work on cutting-edge research, building scalable ML solutions, and improving performance on Nvidia hardware like GPUs and AI accelerators. They collaborate with software and hardware teams to enhance AI capabilities across industries such as gaming, healthcare, and autonomous systems. Strong coding skills in Python, C++, and experience with ML frameworks like TensorFlow or PyTorch are often required.

What are the key skills and qualifications needed to thrive in the Nvidia Machine Learning position, and why are they important?

To thrive in an Nvidia Machine Learning role, a deep understanding of machine learning algorithms, proficiency in programming languages like Python or C++, and a solid background in mathematics or computer science are essential. Experience with Nvidia's CUDA, TensorRT, cuDNN, and familiarity with modern deep learning frameworks such as TensorFlow or PyTorch are highly valued, as are relevant certifications in AI or data science. Strong problem-solving skills, teamwork, and effective communication distinguish top candidates in collaborative, fast-paced environments. These skills are crucial for developing and optimizing AI solutions that leverage Nvidia’s advanced hardware and software platforms.

What are some common challenges faced by professionals in Nvidia Machine Learning roles?

One common challenge in Nvidia Machine Learning roles is optimizing models to fully leverage GPU architectures for both performance and efficiency, which requires continuous learning as the technology rapidly evolves. Team members often work on complex, large-scale projects that demand close collaboration across software, hardware, and research divisions. Navigating the fast pace of innovation and contributing effectively to cross-functional teams is essential for success. However, these challenges also make the role exciting and offer excellent opportunities for professional growth and hands-on experience with state-of-the-art AI solutions.

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Nvidia Machine Learning jobs near you
SOC IP Methodology Engineer - Custom SOC

SOC IP Methodology Engineer - Custom SOC

Nvidia

Austin, TX

Full-time

Posted 19 days ago

Job description

Nvidia is hiring a Senior SOC/IP Methodology Engineer to help design and architect next generation custom SoC/IP solutions. We are looking for individuals with passion and desire to deliver innovative products. Together, we will build the next generation of life changing SoC's. If you are a motivated individual that understands how SoC systems are architected and built, has intimate knowledge of client requirements, and understand various development cycles, this is your place to be.

What you will be doing:

  • Responsible for developing and optimizing semi-custom RTL to GDS methodologies, work with internal and external collaborators and IP Vendors on SOC/IP requirements and drive technology alignments across them.

  • You will be a hands-on domain expert, able to traverse from Synthesis to final design closure (timing and layout) involving latest EDA technologies and capabilities.

  • Work with customers on SOC/IP development processes, IP quality and handoff requirements for QA, smooth integration, and high-quality analysis flows.

  • You will drive, review, and cultivate development processes to assure top quality work to and from IP customers and SOC engineers.

  • You will help in driving technical design reviews, assuring that defined processes are followed, identifying, and mitigating risks, and continuing to improve development processes with innovative tools and procedures.

  • Be responsible for results/handoffs to and from Customers, methodology solutions and schedule plans.

  • You will be required to drive early PPA on customer IP and drive what-if experiments to help drive KPI targets, analyze and solve critical issues

  • Will work with internal and external collaborators to carry out floorplan experiments to drive area estimates and evaluate solution tradeoffs.

  • Work with Nvidia PD design methodology team to come up with best methodologies to integrate external customer IP.

  • You will also work with external ASIC companies if we decide to outsource some part of Nvidia design.

What we need to see:

  • Masters with 8+ (or BS or equivalent experience with 10+) years of experience within these skill areas.

  • Extensive leadership experience as Methodology and Technical expert in physical design working with internal and external partners

  • Experience working in a SOC development and customer focused environment with excellent interpersonal skills.

  • Experience in handling complex IP ecosystem involving both internal and external partners. Exposure to IP-XACT or similar infrastructure is a plus.

  • Proven hands-on experience with RTL-to-GDSII tool flows. physical design and analysis tools from EDA vendors such as Cadence, Synopsys, Mentor (CDC, LP Checks, Genus, First Encounter, Innovus, Design Compiler, Fusion Compiler, ICC2, PT-SI, Tempus, Redhawk) etc.

  • Understanding of full flow (including DFT, BIST) to integrate customer and third-party IP and drive program alignment.

  • Proven abilities to optimize methodology and flows for high productivity, design optimization and incorporate innovation.

  • Strong background and knowledge in Synthesis, CTS, Power Optimization, Placement and Route methods and timing convergence for high performance designs like CPU, GPU and machine learning IPs.

  • Strong scripting skills involving Python, Perl and Tcl, excellent soft skills.

  • RTL2GDS experience with high performance ARM cores, Serdes, DDR, GPU, machine learning experience would be a plus.

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and, due to outstanding growth, our special engineering teams are growing fast. If you're a creative and autonomous professional with a genuine passion for technology, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 24, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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