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Machine Learning Research Scientist Jobs in Texas

The AI Research Scientist will design, train, evaluate, and optimize cutting-edge machine learning models, collaborating with various teams to ensure innovations have real-world impact.

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Machine Learning Research Scientist information

See Texas salary details

$47K

$121.2K

$162.1K

How much do machine learning research scientist jobs pay per year?

As of Aug 4, 2026, the average yearly pay for machine learning research scientist in Texas is $121,224.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $161,200.00 per year, depending on experience, location, and employer.

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

To thrive as a Machine Learning Research Scientist, you need a strong background in mathematics, statistics, programming (typically Python or similar), and experience with machine learning frameworks, usually supported by an advanced degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Scikit-learn, and proficiency in data handling and cloud platforms is highly valued, alongside certifications like Google Cloud ML Engineer as a plus. Innovative thinking, strong communication, and effective problem-solving skills help set exceptional researchers apart. Mastery of both technical and soft skills is essential for developing impactful models, collaborating across multidisciplinary teams, and staying ahead in this fast-evolving field.

What does a machine learning research scientist do?

As a Machine Learning Research Scientist, your typical projects may include developing novel algorithms, conducting experiments on large datasets, implementing and tuning models, and publishing research findings. Your daily responsibilities often involve coding, data analysis, reading the latest literature, collaborating with engineers and product teams, and participating in internal discussions about research direction. You may also mentor junior researchers, contribute to open-source projects, and present results at conferences or internal meetings. This role offers a dynamic work environment where continuous learning and innovative problem-solving are highly encouraged.

What is a machine learning research scientist?

A Machine Learning Research Scientist develops new algorithms and models to advance the field of artificial intelligence. They conduct experiments, analyze data, and publish research to push the boundaries of machine learning theory and applications. Their work often involves designing novel architectures, optimizing existing models, and collaborating with engineers to bring research into production. This role typically requires a deep understanding of mathematics, statistics, and programming, along with experience in areas like deep learning, reinforcement learning, or probabilistic modeling.

What are the most commonly searched types of Machine Learning Research Scientist jobs in Texas? The most popular types of Machine Learning Research Scientist jobs in Texas are:
What cities in Texas are hiring for Machine Learning Research Scientist jobs? Cities in Texas with the most Machine Learning Research Scientist job openings:
Infographic showing various Machine Learning Research Scientist job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $121,224 per year, or $58.3 per hour.

AI Research Scientist

webAI

Austin, TX โ€ข On-site

Full-time

Re-posted 15 days ago


Job description

Job Summary:
webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. The AI Research Scientist will design, train, evaluate, and optimize cutting-edge machine learning models, collaborating with various teams to ensure innovations have real-world impact.
Responsibilities:
โ€ข Design, train, and optimize machine learning models including LLMs, multimodal models, transformers, and diffusion architectures
โ€ข Conduct research on model efficiency, quantization, compression, and on-device deployment
โ€ข Prototype novel model architectures, training methods, and inference strategies for distributed AI
โ€ข Develop and evaluate benchmarks, datasets, and experimental frameworks to test model performance
โ€ข Collaborate with engineering teams to integrate research findings into production systems
โ€ข Stay current on leading research in deep learning, generative AI, and distributed ML
โ€ข Analyze experimental results and communicate insights clearly to technical and non-technical stakeholders
โ€ข Document research findings, contribute to internal papers, and present technical work across the organization
โ€ข Identify emerging technologies and propose research directions aligned with webAIโ€™s strategic priorities
Qualifications:
Required:
โ€ข 4+ years of experience (can be graduate research) in machine learning research, AI model development, or related fields
โ€ข Strong expertise in deep learning architectures including transformers, CNNs, RNNs, and diffusion models
โ€ข Hands-on experience training and fine-tuning large-scale models
โ€ข Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
โ€ข Experience building datasets, designing experiments, and validating ML model performance
โ€ข Deep understanding of optimization techniques including quantization, distillation, pruning, and hardware-aware training
โ€ข Strong problem-solving skills and ability to work independently on complex research tasks
โ€ข Effective communication skills for presenting research findings to diverse audiences
โ€ข Bachelorโ€™s degree in Computer Science, Engineering, Mathematics, or a related field
Preferred:
โ€ข Masterโ€™s or PhD in Machine Learning, Computer Science, AI, or a related field
โ€ข Experience with distributed training, edge inference, or on-device ML
โ€ข Research experience in generative AI, reinforcement learning, or multimodal learning
โ€ข Familiarity with privacy-preserving ML techniques such as federated learning
โ€ข Experience contributing to academic publications, patents, or open-source ML projects
โ€ข Comfort operating in a fast-paced, high-growth startup environment
Company:
The leader in private AI. Founded in 2020, the company is headquartered in Austin, USA, with a team of 51-200 employees. The company is currently Growth Stage.