Summer Internship - Machine Learning Engineer job opportunity at WebAI.

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WebAI Summer Internship - Machine Learning Engineer
Experience: General
Pattern: Intern
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Engineering

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degreeBachelor's (B.Sc.)
loacation Austin, TX, United States Of America
loacation Austin, TX....United States Of America

About Us We are establishing the first distributed Al infrastructure dedicated to personalized Al. The evolving needs of a data-driven society are demanding scalability and flexibility. We believe that the future of Al is distributed and enables real-time data processing at the edge, closer to where data is generated. We are building a future where a company's data and IP remains private and it's possible to bring large models directly to consumer hardware without removing information from the model. About the Role We are seeking a highly motivated and enthusiastic Machine Learning Engineer Intern to join our dynamic team for the summer. In this role, you will gain hands-on experience in developing and implementing machine learning models, data pipelines, and analytical solutions. Working alongside experienced professionals, you will have the opportunity to contribute to live projects and see the direct impact of your work. Key Responsibilities Applied AI: Assist in designing, training, and evaluating machine learning models for real-world use cases. RAG Systems: Help build contextualized models and tools for efficient data ingestion and transformation. Research & Analysis: Perform exploratory data analysis and stay updated on the latest ML research, techniques, and tools. Collaborative Problem-Solving: Work closely with cross-functional teams (Data Scientists, Software Engineers, Product Managers) to define project requirements and deliver solutions. Documentation & Reporting: Prepare clear, concise technical documentation and report findings to relevant stakeholders. Quality Assurance: Contribute to code testing processes to ensure robust, high-quality deliverables. Qualifications Currently enrolled in a Bachelor's, Master's, or PhD program in Computer Science, Engineering, Statistics, Mathematics, or a related field. Foundational knowledge of machine learning concepts, data structures, and algorithms. Proficiency in programming languages such as Python or C++. Experience with ML libraries/frameworks such as TensorFlow, PyTorch, ONNX, JAX or equivalent Strong problem-solving and analytical skills, with a keen eye for detail. Excellent communication skills and the ability to work collaboratively in a team environment. Core Values We at webAl are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth: Emphasizing transparency and honesty in every interaction and decision. Ownership: Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients. Tenacity: Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility: Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits Competitive Pay Free parking at our office in downtown Austin Equal Opportunity Employer webAl is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAl to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

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