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 ...
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 ...
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. • ...
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. • ...
... 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 ...
... 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 ...
Machine Learning Engineer
Austin, TX · On-site
Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...
Machine Learning Engineer
Austin, TX · On-site
Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...
Machine Learning Engineer
Austin, TX · On-site
Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...
Machine Learning Engineer
Austin, TX · On-site
Additionally, you will analyze the latest research, assess the applicability of emerging deep ... Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ...
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 ...
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 ...
Machine Learning Engineers here build analytical solutions and think deeply about where they fit ... a research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd ...
Machine Learning Engineers here build analytical solutions and think deeply about where they fit ... a research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd ...
Machine Learning Engineers here build analytical solutions and think deeply about where they fit ... a research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd ...
Machine Learning Engineers here build analytical solutions and think deeply about where they fit ... a research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd ...
Machine Learning Researcher - PhD Intern (US)
Houston, TX · On-site
$4.5K - $5.8K/wk
Machine Learning Researchers play a key role in this mission by developing next-generation models ... You'll get to challenge the impossible in quantitative research by applying sophisticated and ...
Machine Learning Researcher - PhD Intern (US)
Houston, TX · On-site
$4.5K - $5.8K/wk
Machine Learning Researchers play a key role in this mission by developing next-generation models ... You'll get to challenge the impossible in quantitative research by applying sophisticated and ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
AI Research Director
Austin, TX · On-site
Master's or PhD in Machine Learning, Computer Science, AI, Mathematics, or related field ... Experience with privacy-preserving AI such as federated learning or secure execution * Strong ...
AI Research Director
Austin, TX · On-site
Master's or PhD in Machine Learning, Computer Science, AI, Mathematics, or related field ... Experience with privacy-preserving AI such as federated learning or secure execution * Strong ...
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 ...
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 ...
Machine Learning Software Engineer II
Dallas, TX · On-site +1
$89K - $123K/yr
Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and ... Job Overview: We are seeking a talented Machine Learning Engineer II to join our CAI machine ...
Machine Learning Software Engineer II
Dallas, TX · On-site +1
$89K - $123K/yr
Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and ... Job Overview: We are seeking a talented Machine Learning Engineer II to join our CAI machine ...
Machine Learning Engineer
Frisco, TX · On-site
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 ...
Machine Learning Engineer
Frisco, TX · On-site
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 ...
... research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd add.
... research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd add.
... research and experimentation to advance machine learning capabilities Collaborate with cross-functional teams to integrate AI solutions into production environments Analyze large datasets to extract ...
... research and experimentation to advance machine learning capabilities Collaborate with cross-functional teams to integrate AI solutions into production environments Analyze large datasets to extract ...
Machine Learning Engineer
Austin, TX · On-site
... research and experimentation to advance machine learning capabilities • Collaborate with cross-functional teams to integrate AI solutions into production environments • Analyze large datasets to ...
Machine Learning Engineer
Austin, TX · On-site
... research and experimentation to advance machine learning capabilities • Collaborate with cross-functional teams to integrate AI solutions into production environments • Analyze large datasets to ...
Our Machine Learning Internship provides a unique opportunity to work with fundamental market data ... Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics ...
New
Our Machine Learning Internship provides a unique opportunity to work with fundamental market data ... Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics ...
New
Research Machine Learning Federated Learning information
What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?
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?
| Aspect | Research Machine Learning Federated Learning | Data Scientist |
|---|---|---|
| Credentials | Advanced degrees in CS, ML, or related fields; research experience | Bachelor's or Master's in Data Science, Statistics, or related fields |
| Work Environment | Research labs, academic institutions, tech companies focusing on privacy-preserving ML | Business environments, analytics teams, data-driven departments |
| Industry Usage | Developing federated algorithms, privacy-preserving ML models | Data 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:
- Senior Machine Learning Engineer Biotech
- Hourly Remote Machine Learning Engineer
- Embedded Machine Learning Engineer
- Internship Machine Learning Engineer New Grad
- Freelance Edge Ai Machine Learning
- Contract Machine Learning Research Scientist
- Internship Applied Scientist Machine Learning
- Machine Learning Engineer Apprenticeship
- Machine Learning Research Scientist
- Lead Machine Learning Engineer
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:
Full-time
Posted 4 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.
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.