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Nvidia Machine Learning Jobs in Texas (NOW HIRING)

ForeFlight

Staff Machine Learning Engineer

Austin, TX · On-site +1

$208K - $255K/yr

Jeppesen ForeFlight is seeking a Senior Machine Learning Engineer to help build and scale domain ... Familiarity with NVIDIA NeMo, Kaldi, ESPnet, Hugging Face, Whisper, DeepSpeed, or equivalent ...

Nvidia

Senior Compiler Engineer - AI

Austin, TX

$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 ...

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Nvidia Machine Learning information

See Texas salary details

$23.8K

$39.7K

$82K

How much do nvidia machine learning jobs pay per year?

As of Jul 22, 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.

How much do NVIDIA machine learning engineers make?

NVIDIA machine learning engineers typically earn between $100,000 and $160,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized expertise in deep learning and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in competitive tech environments.

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.

Does NVIDIA do machine learning?

Nvidia offers extensive tools and platforms for machine learning, including GPUs optimized for training and deploying models. Many machine learning engineers and researchers use Nvidia hardware and software frameworks like CUDA and cuDNN to accelerate AI development. The company also provides AI-focused products and solutions for various industries.

Is it hard to get hired at NVIDIA?

Getting hired for a machine learning role at NVIDIA can be competitive due to the company's focus on advanced technology and innovation. Candidates typically need strong technical skills in deep learning, programming, and relevant experience, along with a solid educational background. The hiring process often involves multiple interviews and technical assessments to evaluate expertise and problem-solving abilities.

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.

Is ML a high paying job?

Machine Learning roles, including positions like Nvidia Machine Learning engineers, are generally well-paid due to high demand for specialized skills in AI, data analysis, and programming. Salaries vary based on experience, location, and company, but these jobs tend to offer above-average compensation compared to many other tech roles.
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Nvidia Machine Learning jobs near you
Infographic showing various Nvidia Machine Learning job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $39,673 per year, or $19.1 per hour.
Real Time Machine Learning (PaaS)

Real Time Machine Learning (PaaS)

Ruri Software Technologies LLC

Irving, TX • On-site

Full-time

Posted 3 days ago

Job description

Job Title: Real Time Machine Learning (PaaS)
Duration: 8+ months
Work Location: Irving TX
Job Description:
  • Strong experience in Kubernetes and distributed computing for real time Al applications (CKAD, CKA certification preferred)
  • ⁠Experience with advanced ML, DL frameworks including TF,Pytorch,Ray
  • ⁠Hands on Experience with CUDA/NVIDIA ecosystem
  • ⁠Exposure to Real time monitoring frameworks including but not limited to Prometheus, Grafana, Open Telemetry (ML Democratization)
  • ⁠Strong experience working with Model Development, ML pipeline at scale with distributed computing technologies lik Pyspark, Ray
  • Ability to work on Distributed training pipelines and be aware of end to end CICD process for Al/ML workloads
  • Orchestrate the model scoring pipelines using Cloud Composer
    Deploy Auto-ML solutions in the production systems
  • ⁠Awareness and hands-on Data Lake, Big data ecosystems
  • ⁠Exposure to containers, cloud native solutioning

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