Research Assistant - MLD - School of Computer Science

Carnegie Mellon University • Pittsburgh, PA

8h

About The Position

Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the curious to deliver work that matters, your journey starts here! The Machine Learning Department (MLD) is a leading hub for research and education in artificial intelligence and machine learning. It focuses on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of undergraduate and graduate programs, fostering a collaborative environment that bridges theoretical research and practical applications. Faculty and students frequently collaborate with industry and other academic disciplines to push the boundaries of what is possible with machine learning. We are seeking a Research Associate. The position performs fundamental research in structured representation learning. They will work on creating AI systems that can learn not just statistical patterns, but also understand how different variables influence each other, similar to how humans naturally reason about causes and effects. Flexibility and cultural sensitivity are valued proficiencies at CMU. Therefore, we are in search of a team member who can optimally interact with a varied population of diverse audiences. We are looking for someone who shares our values and who will support the mission of the university through their work.

Requirements

• Bachelor's Degree
• Programming experience in Python, PyTorch and Tensorflow.
• Familiarity with data analysis and machine learning concepts.
• A combination of education and meaningful experience from which comparable knowledge is proven may be considered.
• Successful background check investigation

Responsibilities

• Implement and test approaches for representation learning using modern deep learning frameworks like PyTorch or TensorFlow.
• Design and run experiments to evaluate approaches on both synthetic and real-world datasets.
• Collaborate with team members to debug models, analyze results, and iterate on research directions through regular code reviews and research discussions.
• Document research findings through clear technical writing, including experiment logs, methodology descriptions, and contributions to research papers.
• Participate in regular research meetings to present findings, discuss relevant papers, and contribute to brainstorming sessions.

Benefits

• Joining the CMU team opens the door to an array of exceptional benefits. Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions.
• Unlock your potential with tuition benefits , take well-deserved breaks with ample paid time off and observed holidays , and rest easy with life and accidental death and disability insurance.
• Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access , and much more!
• For a comprehensive overview of the benefits available, explore our Benefits page .
• At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level