Data Science & Machine Learning Intern 

Location: Hybrid – Greater Boston Area 
Duration: 4-6 Month Internship 
Compensation: Paid Internship ($45–$60/hour depending on experience) 
Potential for Full-Time Employment Following Internship 

About CranioSense 

CranioSense is an early-stage medical device company developing novel, non-invasive technology to assess elevated intracranial pressure and neurological health. Our platform combines advanced sensing technologies, physiological waveform analysis, and machine learning to generate clinically meaningful insights that can improve patient care. Backed by venture capital, grant funding, and strategic research collaborations, CranioSense operates at the intersection of medtech, data science, and biomedical innovation. 

Position Overview 

CranioSense is seeking a highly motivated PhD-level Data Science & Machine Learning Engineer to support development of advanced physiological algorithms from complex optical-derived waveform and sensor datasets. The ideal candidate has experience extrapolating robust biomarkers and features from noisy raw physiological data and analytical pipelines suitable for predictive ML modeling and algorithm development. 
 
This role is intended for candidates who are nearing compleof orf, or have recently completed (within last 24 months), a PhD and are interested in translating advanced research into real-world biomedical applications. The intern will work closely with CranioSense’s VP of Data Science and report directly to the COO. The role offers significant responsibility and independence while providing mentorship from experienced technical and business leaders. 
 
This internship is designed as a high-impact, hands-on opportunity with strong potential for conversion into a full-time role following successful completion of the internship. 

Key Responsibilities 

  • Develop and optimize machine learning (ML) and advanced (bio-) signal processing algorithms using physiological waveform and sensor datasets 

  • Develop and validate ML/signal-processing algorithms specifically for estimating intracranial pressure (ICP) from PPG waveforms, building on and extending existing approaches 

  • Design preprocessing and signal conditioning pipelines to prepare, filter, normalize, and analyze raw physiological data streams 

  • Interpret patterns in noisy real-world physiological data and derive robust, reproducible features suitable for predictive modeling and algorithm development  

  • Contribute biophysical/mechanistic reasoning (not just black-box modeling) to guide feature selection: grounding ML choices in the underlying relationship between hemodynamic pressure changes and PPG waveform characteristics 

  • Build and evaluate statistical, machine learning, and deep learning models for physiological signal interpretation 

  • Support development of scalable cloud-based data science and algorithm workflows 

  • Collaborate cross-functionally with product, engineering, and leadership teams 

  • Review and evaluate relevant scientific literature, emerging methodologies, and commercially available approaches 

  • Present technical findings and concepts clearly to both technical and non-technical stakeholders 

  • Operate independently within a fast-paced startup environment while contributing effectively within a collaborative team 

Preferred Qualifications 

  • Candidate must be nearing completion of, or have recently completed (within last 24 months), a PhD in Biomedical Engineering, Electrical Engineering, Computer Science, Data Science, Applied Mathematics, Physics, Computational Biology, or a related quantitative discipline 

  • Strong experience developing machine learning or advanced analytical algorithms from physiological waveform, biomedical sensor, or time-series datasets 

  • Experience with biomedical signal processing, feature extraction, and intelligent preprocessing of noisy physiological data 

  • Experience with biophotonics datasets or optical sensing data preferred 

  • Strong proficiency in Matlab and Python and scientific computing / machine learning frameworks 

  • Experience with cloud-based machine learning development environments (AWS, Azure, GCP) preferred 

  • Ability to independently structure and execute technical research projects 

  • Strong analytic, inferencing, critical thinking, and creative problem-solving skills 

  • Excellent written and verbal communication skills 

Why Join CranioSense? 

  • Work on cutting-edge biomedical technology with real clinical impact 

  • Collaborate directly with experienced startup, product, and data science leaders 

  • Gain hands-on experience in translational biomedical algorithm development 

  • Operate in a fast-moving, innovation-driven startup environment 

  • Opportunity for long-term employment following internship completion 

MLSC Internship Program 

Preference may be given to candidates registered through the Massachusetts Life Sciences Center (MLSC) Data Science Internship Program. 

Apply Now