ML Performance Engineer - Drug Discovery

2w2 weeks ago

Northeastern University

Boston, US · Temporary · $75,210 – $106,230

About this role

Northeastern University's Institute for Experiential AI seeks an ML Performance Engineer for the AI and Life Sciences team. This 1-year fixed-term position involves applied research in neural networks for drug discovery. Renewal is based on available external funding availability.

Develop AI algorithms for drug synergies using multimodal datasets with molecular features of drugs and biological fingerprints of cancer cell lines. Conduct scientific programming, data analysis, and efficacy/potency predictions for drug combinations. Prepare work for journal and conference publications.

Work closely with Ayan Paul and Hyunju Kim at EAI, mentoring graduate students on the project. Collaborate with academic labs at Northeastern and industry partners. Participate in meetings with collaborators to advance research goals.

Join a pioneering hub combining human and machine intelligence for societal impact. Contribute to extramural funding applications in a unique culture of high-impact AI research. Shape human-machine collaboration in life sciences from the Boston campus.

Requirements

MS in computer science, computational biology, or bioinformatics with heavy focus on machine learning and AI model training/development
About 1 year of research or work experience in an academic group, corporate if aligned with role
Record of outstanding research evidenced by software outputs and scholarly measures of impact
Experience with neural networks applied to biological data
Proficiency in scientific programming for AI model development
Familiarity with multimodal datasets in drug discovery

Responsibilities

Conduct applied research on neural networks for efficacy and potency prediction of drug combinations
Develop AI algorithms for drug synergies using public and proprietary multimodal datasets
Perform scientific programming and data analysis on molecular features and cancer cell line fingerprints
Prepare manuscripts for journal and conference publications
Contribute to extramural funding applications
Mentor graduate students working on related projects
Meet with academic and industry collaborators
Analyze multimodal datasets including biological fingerprints