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Research Federated Learning Jobs in Texas (NOW HIRING)

webAI

AI Research Scientist

Austin, TX · On-site

The AI Research Scientist will design, train, evaluate, and optimize cutting-edge machine learning ... federated learning • Experience contributing to academic publications, patents, or open-source ML ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

webAI

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

webAI Inc

AI Research Scientist

Austin, TX · On-site

The AI Research Scientist will contribute to webAI's development of next-generation AI models and ... Familiarity with privacy-preserving ML techniques such as federated learning * Experience ...

webAI Inc

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Productionize AI models from research prototypes into scalable, deployable systems used in real ... Experience with edge AI, federated learning, or offline inference systems. * Understanding of AI ...

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Research Federated Learning information

What is a researcher in federated learning?

A Researcher in Federated Learning is a professional who studies, develops, and improves federated learning algorithms and systems. Federated learning is a machine learning approach where data remains decentralized, allowing multiple devices or organizations to collaboratively train models without sharing raw data. These researchers focus on advancing privacy, efficiency, and performance in distributed AI systems. Their work often involves experimenting with new methods, publishing findings, and contributing to the growing field of privacy-preserving machine learning.

What are the key skills and qualifications needed to thrive as a researcher in federated learning?

To thrive as a Researcher in Federated Learning, you need a strong background in machine learning, distributed systems, and statistics, typically supported by an advanced degree in computer science or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow Federated, and experience with privacy-preserving algorithms are essential. Critical thinking, collaboration, and effective communication are key soft skills for designing experiments and sharing findings with peers. These competencies are vital for advancing privacy-aware AI solutions and producing impactful research in this rapidly evolving domain.

What are some common challenges faced by professionals working in research federated learning, and how can they be addressed?

Professionals in Research Federated Learning often encounter challenges such as ensuring data privacy across distributed devices, managing non-iid (non-independent and identically distributed) data, and optimizing communication efficiency between clients and servers. Addressing these issues requires strong collaboration with cross-functional teams, including data engineers, security experts, and software developers, to develop robust protocols and algorithms. Staying updated with the latest research and participating in open-source collaborations can also help overcome technical hurdles and drive innovation in this rapidly evolving field.

What is the difference between Research Federated Learning vs Data Scientist?

AspectResearch Federated LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm developmentBusiness environments, analytics teams; focus on data analysis and insights
Industry UsageAI research, privacy-preserving ML, distributed systemsBusiness intelligence, marketing, finance, healthcare

Research Federated Learning involves developing privacy-focused, distributed machine learning algorithms, often in research or specialized tech settings. Data Scientists analyze data to generate insights and support decision-making in various industries. While both roles require strong analytical skills, Research Federated Learning emphasizes algorithm development and privacy, whereas Data Scientists focus on data analysis and reporting.

What are popular job titles related to Research Federated Learning jobs in Texas?

For Research Federated Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Research Federated Learning jobs in Texas look for?

The top searched job categories for Research Federated Learning jobs in Texas are:

What cities in Texas are hiring for Research Federated Learning jobs?

Cities in Texas with the most Research Federated Learning job openings:

Research Federated Learning jobs near you
Infographic showing various Research Federated Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI Research Scientist

webAI

Austin, TX • On-site

Full-time

Re-posted 7 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:
• Design, train, and optimize machine learning models including LLMs, multimodal models, transformers, and diffusion architectures
• Conduct research on model efficiency, quantization, compression, and on-device deployment
• Prototype novel model architectures, training methods, and inference strategies for distributed AI
• Develop and evaluate benchmarks, datasets, and experimental frameworks to test model performance
• Collaborate with engineering teams to integrate research findings into production systems
• Stay current on leading research in deep learning, generative AI, and distributed ML
• Analyze experimental results and communicate insights clearly to technical and non-technical stakeholders
• Document research findings, contribute to internal papers, and present technical work across the organization
• Identify emerging technologies and propose research directions aligned with webAI’s strategic priorities
Qualifications:
Required:
• 4+ years of experience (can be graduate research) in machine learning research, AI model development, or related fields
• Strong expertise in deep learning architectures including transformers, CNNs, RNNs, and diffusion models
• Hands-on experience training and fine-tuning large-scale models
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
• Experience building datasets, designing experiments, and validating ML model performance
• Deep understanding of optimization techniques including quantization, distillation, pruning, and hardware-aware training
• Strong problem-solving skills and ability to work independently on complex research tasks
• Effective communication skills for presenting research findings to diverse audiences
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field
Preferred:
• Master’s or PhD in Machine Learning, Computer Science, AI, or a related field
• Experience with distributed training, edge inference, or on-device ML
• Research experience in generative AI, reinforcement learning, or multimodal learning
• Familiarity with privacy-preserving ML techniques such as federated learning
• Experience contributing to academic publications, patents, or open-source ML projects
• 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 201-500 employees. The company is currently Growth Stage.

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