Research Associate - School of Computer Science - MLD

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) at Carnegie Mellon University 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.

MLD is seeking a Research Associate to conduct research in Casual Representation Learning for anomaly detection.

Core 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.

Adaptability, excellence, and passion are vital qualities within Carnegie Mellon University.  We are in search of a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.

Qualifications:

• Bachelor’s Degree required. Master’s Degree preferred.
• Previous research experience preferred.
• Strong analytical, communication, problem-solving, and reasoning skills.
• A combination of education and proven experience from which comparable knowledge is demonstrated may be considered.

Requirements:

• Successful completion of a pre-employment background check

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.

Are you interested in an exciting opportunity with an exceptional organization?! Apply today!

Location

Pittsburgh, PA

Job Function

Researchers

Position Type

Staff – Fixed Term (Fixed Term)

Full Time/Part time

Full time

Pay Basis

Hourly

More Information: