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Deep Learning Scientist Jobs in Texas (NOW HIRING)

Role: Data Scientist Location: Irving, TX - Fulltime Position Data Scientist with Generative AI ... Machine learning, NLP & deep learning: Strong understanding of supervised and unsupervised learning ...

Build NLP pipelines using deep learning frameworks such as PyTorch , TensorFlow , or similar ... Core Data Science Engineering & MLOps * Work in Databricks for ETL, feature engineering, ML ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

As a Research Scientist Intern at Whiterabbit.ai, you will: * Play a key role in architecting the ... Train on a dedicated high-performance compute cluster specialized for deep learning research * Work ...

Showing results 41-60

Deep Learning Scientist information

See Texas salary details

$34.9K

$114.3K

$183.1K

How much do deep learning scientist jobs pay per year?

As of Sep 3, 2026, the average yearly pay for deep learning scientist in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

What is a deep learning scientist?

Deep Learning Scientists are experts who design, develop, and implement advanced machine learning models inspired by the structure and function of the brain, known as artificial neural networks. They work with large datasets to train algorithms that can recognize patterns, make predictions, and solve complex problems in areas such as image recognition, natural language processing, and autonomous systems. Deep Learning Scientists often collaborate with software engineers, data scientists, and domain specialists to deploy models in real-world applications like healthcare, finance, and self-driving cars.

What are the key skills and qualifications needed to thrive as a deep learning scientist?

To thrive as a Deep Learning Scientist, you need a solid background in machine learning, statistics, and programming, often supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience with cloud computing platforms, and proficiency in Python are typically required. Strong problem-solving skills, creativity, and the ability to communicate complex ideas clearly set outstanding candidates apart. These capabilities are essential for developing innovative AI solutions, interpreting results, and collaborating effectively in multidisciplinary teams.

What are some typical challenges faced when working as a deep learning scientist, and how can they be addressed?

Deep Learning Scientists often encounter challenges such as managing large datasets, tuning complex model architectures, and ensuring reproducibility of experiments. Handling these issues requires strong skills in data preprocessing, familiarity with version control systems, and experience with frameworks like TensorFlow or PyTorch. Collaborating closely with cross-functional teams—including data engineers, software developers, and domain experts—can also help in overcoming technical and project-related obstacles. Continuous learning and staying updated with the latest research is essential to excel in this rapidly evolving field.

What is the difference between Deep Learning Scientist vs Machine Learning Engineer?

AspectDeep Learning ScientistMachine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related fields; strong background in deep learning frameworksBachelor's or Master's in Computer Science or related fields; proficiency in machine learning algorithms and software engineering
Work EnvironmentResearch-focused, experimental, often in R&D teamsDevelopment and deployment-focused, working on production systems
Employer & Industry UsageTech companies, research labs, AI startupsTech firms, finance, healthcare, and industries deploying ML models

While both roles involve machine learning, Deep Learning Scientists focus on developing advanced neural network models and research, whereas Machine Learning Engineers implement, optimize, and deploy these models in real-world applications.

What cities in Texas are hiring for Deep Learning Scientist jobs?

Cities in Texas with the most Deep Learning Scientist job openings:

Infographic showing various Deep Learning Scientist job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 2% Contract, and 1% Nights. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

Principal Deep Learning Communication Architect

Nvidia

Austin, TX

Full-time

Re-posted 22 days ago


Key responsibilities

  • Define the long-term technical roadmap for communication libraries across NVIDIA's platforms and ensure their scalability.

  • Lead the development and optimization of communication primitives and collective algorithms for heterogeneous interconnects.

  • Partner with application developers and collaborate with hardware/software teams to influence design and ensure communication libraries meet the requirements of large-scale AI workloads.


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies


Job description

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. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

What You'll Be Doing:

  • Architecture Leadership:Define the long-term technical roadmap for communication libraries across NVIDIA's next-generation platforms. You will ensure the seamless scaling of models to clusters comprising hundreds of thousands of nodes.

  • AI Communication Library Design:Lead the development of next-generation communication primitives and collective algorithms. This includes optimizing for heterogeneous interconnects such as NVLink, Spectrum-X (Ethernet), and Quantum-X (InfiniBand).

  • Application- Communication Library Co-Design:Partner with application developers to architect and implement specialized communication primitives. You will ensure that AI and HPC libraries-includingNCCL, NIXL, NVSHMEM, UCC, and UCX-evolve to meet the requirements of trillion-parameter and Agentic AI.

  • Hardware/Software Co-Design:Collaborate with silicon Aarchitects and software engineers to influence hardware specifications for next-generation networking, ensuring they meet the evolving demands of trillion-parameter LLMs and Agentic AI.

  • Quantitative Modeling:Develop high-fidelity analytical models and simulators to predict system behavior under emerging workloads.

What We Need to See:

  • Ph.D. or M.S. in Computer Science, Electrical Engineering, or a related field (or equivalent experience), with 12+ years of industry experience in high-performance computing (HPC) or distributed deep learning.

  • Parallelism Expertise:Deep understanding of 3D parallelism (Data, Tensor, Pipeline) and advanced strategies including Context Parallelism, Expert Parallelism, and Zero Redundancy Optimizer (ZeRO) variants.

  • Technical Proficiency:Deep technical proficiency with NCCL, UCX, UCC, NVSHMEM, or MPI. Experience with RDMA, RoCE, and low-level InfiniBand verbs is required.

  • Inference & Serving:Advanced knowledge of high-throughput inference engines and schedulers, specifically TensorRT-LLM, vLLM, SGLang, and NVIDIA Dynamo.

  • GPU Architecture:Expert knowledge of the NVIDIA GPU memory hierarchy (HBM3e/HBM4, L2 cache) and CUDA programming models.

Ways to Stand Out from the Crowd:

  • Framework Development:Hands-on experience developing within Megatron-Core, DeepSpeed, or JAX/XLA, with an understanding of how these frameworks interact with low-level communication runtimes is a plus.

  • Significant upstream contributions to major open-source projects (e.g., PyTorch Distributed, KServe, or Ray).

  • A proven track record of deploying and optimizing models on NVIDIA platforms or similar rack-scale systems.

  • A strong portfolio of patents or papers in top-tier systems/architecture venues (e.g., ISCA, ASPLOS, NeurIPS, SC).

With competitive salaries and a generous benefits package, we are 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 unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services 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 272,000 USD - 431,250 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until April 18, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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