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Research Machine Learning Federated Learning Jobs in Texas

webAI

AI Research Scientist

Austin, TX · On-site

Responsibilities : • Design, train, and optimize machine learning models including LLMs ... federated learning • Experience contributing to academic publications, patents, or open-source ML ...

HEB

Data Scientist - Demand Forecasting

San Antonio, TX · On-site

$158K/yr

... machine learning process, and orchestrating reusable storytelling methodology to apply toward AI ... research to accelerate business innovation What is your background? - A related degree or ...

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. • ...

Socket.dev

Data Scientist - eCommerce

San Antonio, TX · On-site

$120 - $180/hr

... machine learning process, and orchestrating reusable storytelling methodology to apply toward AI ... Applies AI research to accelerate business innovation What is your background? * A related degree ...

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Research, analyze, support, and implement machine learning solutions on the Snowflake Cloud data warehouse platform using the Snowpark framework Develop novel solutions using knowledge of the latest ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Formulate research questions to guide the development of neural networks and signal processing ... for machine learning applications for BCI. * Lead the team by performing at a high standard ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Work closely with researchers, software engineers, and robotics experts to integrate machine learning solutions into real-world autonomous systems. Qualifications : Required : • Strong ...

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 ...

webAI Inc

AI Research Scientist

Austin, TX · On-site

Design, train, and optimize machine learning models including LLMs, multimodal models, transformers ... Familiarity with privacy-preserving ML techniques such as federated learning * Experience ...

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team ... Strong communicator who can translate complex research findings into actionable decisions for ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Texas

Research Machine Learning Federated Learning information

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

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

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

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

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

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

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

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

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

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

Research Machine Learning Federated Learning jobs near you

AI Research Scientist

webAI

Austin, TX • On-site

Full-time

Re-posted 25 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 51-200 employees. The company is currently Growth Stage.

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