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Deep Learning Scientist Jobs in Texas (NOW HIRING)

Avride

Machine Learning Engineer

Austin, TX

About the team Avride develops autonomous vehicle and delivery robot technology, leveraging deep ... Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience ...

Avride

Machine Learning Engineer

Austin, TX · On-site

About the team Avride develops autonomous vehicle and delivery robot technology, leveraging deep ... Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience ...

Sarian, Inc.

Sr. Data Scientist

Irving, TX · On-site

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 ...

Vytwo

Data Scientist - INDIA

Prosper, TX · On-site

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 ...

Whiterabbit.ai

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 ...

Whiterabbit.ai

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 ...

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Showing results 21-40

All JobsDeep Learning Scientist JobsDeep Learning Scientist Jobs in Texas

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 Aug 10, 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 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 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 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 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 are popular job titles related to Deep Learning Scientist jobs in Texas? For Deep Learning Scientist jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Deep Learning Scientist jobs in Texas look for? The top searched job categories for Deep Learning Scientist jobs in Texas are:
What cities in Texas are hiring for Deep Learning Scientist jobs? Cities in Texas with the most Deep Learning Scientist job openings:
Deep Learning Scientist jobs near you
Infographic showing various Deep Learning Scientist job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

Senior Deep Learning Framework Communications Engineer

Nvidia

Austin, TX

Full-time

Re-posted 19 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.

We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision?

What you will be doing:

  • Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production

  • Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models.

  • Improve AI compilers to hide communications or perform automatic fusion.

  • Conduct in-depth AI workload performance characterization on multi-GPU clusters.

  • Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.

  • Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms.

  • Influence the roadmap of communication libraries - NCCL & NVSHMEM.

  • Collaborate with a very dynamic team across multiple time zones.

What we need to see:

  • B.S, M.S. or PHD in Computer Science, or related field (or equivalent experience) with 5+ software engineering and HPC/AI experience

  • Development or integration experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang

  • Rapid prototyping and development with Python, C++, CUDA or related DSLs (Triton, cuTe)

  • Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile)

  • Experience conducting performance benchmarking on AI clusters. Familiarity with at least one performance profiler toolchain (PyTorch profiler, NVIDIA Nsight Systems)

  • Understanding of HPC/AI communication concepts (1-sided v 2-sided communication, elasticity, resiliency, topology discovery, etc)

  • Adaptability and passion to learn new areas and tools

  • Flexibility to work and communicate effectively across different teams and timezones

Ways to stand out from the crowd:

  • Experience with parallel programming on at least one communication runtime (NCCL, NVSHMEM, MPI). Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)

  • Expertise in one or more of these areas: Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cuTe, etc). Experience with programming for compute & communication overlap in distributed runtimes

  • Experience with AI compiler pattern matching and lowering. Solid understanding of memory hierarchy, consistency model, and tensor layout

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 12, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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

Year founded

1993

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