ML Performance Engineer, Knowledge Graphs

About the Opportunity

This job description is intended to describe the general nature and level of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. 

About the Institute

Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions?

We are building a pioneering research and innovation hub in AI—one that will shape the way humans and machines collaborate for decades to come. Led by Prof. Alan Mislove, the Institute for Experiential AI is built around the challenges and opportunities made possible by human-machine collaboration. The Institute provides a framework to design, implement, and scale AI-driven technologies in ways that make a true difference to society. Our ability to respond to the opportunities afforded to society will depend on training and building a workforce that is AI-capable and prosperous.

Founded in 1898, Northeastern is a global research university and the recognized leader in experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus.

The Culture

Here at the Institute for Experiential AI (EAI) we are committed to the highest standards in all we do. Working at the EAI offers opportunities, an environment, and a culture that just isn’t found together anywhere else. This is the right place for you if you’re curious, motivated by the future of technology, and want to be part of a unique and diverse community that works on high-impact research, educational, business, and societal problems.

Job Summary:

This is a 1-year fixed-term position, eligible for renewal pending available funding. Northeastern University's Institute for Experiential AI has established a position for a ML engineer with the AI and Life Sciences team. The ML Engineer will be based out of the Boston campus, MA, conduct applied research, prepare work for submission to journal/conference publications, and contribute to extramural funding applications. This position has a three-year appointment, renewable yearly subject to availability of funding. The ML Engineer will work on projects that will include developing knowledge graphs based on natural language processing and LLMs. Participation in these projects will include scientific programming, data analysis, manuscript preparation, meeting with collaborators, mentoring graduate students, etc.

In particular, the ML Engineer hired for this position will work with Benjamin Gyori at Khoury College and Ayan Paul at EAI and is expected to develop AI algorithms for LLM based RAG systems, work with graduate students working on the same project, and collaborate with academic and industry partners of EAI.

Minimum Qualifications:

• An MS in computer science with a heavy focus on machine learning and LLM-RAG construction and development by the appointment start date.

• About 1 year of research or work experience in an academic group. Corporate experience will be considered if it is aligned with the job role

• A record of outstanding research, as evidenced by software outputs, and other scholarly measures of impact.

• Strong demonstrable background in machine learning. Must have demonstrable experience in LLM and knowledge graphs.

• Must have 1+ years of experience in using Neo4j and high performance computing.

• Must have familiarity with knowledge graphs and metrics for assessing performance of RAG systems.

• Must have experience in research software development, FAIR data/open science, life sciences data systems, and analysis of various kinds of ‘omics data (e.g., metabolomics, proteomics, genomics, transcriptomics, etc.).

Values & Abilities:

• We expect ML Engineers to establish and advocate for best practices in scientific reproducibility of all results, and to promote the ethical use of data and algorithms.

• Excellent written and verbal communication skills.

• Respect for diversity and the importance of interdisciplinary teams.

• Entrepreneurial mindset, self-starter, and innovative thinker while at the same time being able to navigate multi-layered channels within a university setting and work across multiple projects.

• Team-player who can collaborate effectively in a university setting and with corporate partners.

• Open-minded, assertive, and professional when collaborating and working within our team and with other groups within Northeastern University.

Key Responsibilities & Accountabilities

• (90%) Co-develop, collaboratively lead and participate in research projects that advance scientific knowledge and solve relevant societal and industrial problems. Participation in these projects will include working with graduate or undergraduate students, scientific programming, data analysis, manuscript preparation, meeting with collaborators, etc.

• (5%) Represent the university in conferences, seminars, and other public events.

• (5%) Other assignments as deemed necessary

Required Documents:

Please submit the following documents in a single Word/PDF document along with your application:

1.     Cover Letter

2.     Resume (Please include contact information of referees for at least three letters of reference)

Position Type

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.  

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

Compensation Grade/Pay Type:

Expected Hiring Range:

With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.