THE BASICS

• This is a full-time position. This posting is for a current vacancy.
• This is a remote-friendly position for candidates based in Canada or the United States. We’d prefer to hire candidates within reasonable commuting distance to our Toronto, Boston, or Bay-Area offices, but it’s not a requirement.
• Work hours are flexible, but meetings are typically scheduled between 9 am and 5 pm Eastern Standard Time.
• The salary range for this role is $175,000 - $200,000, dependent on location.
• The hiring manager for this position is Keshav Datta.

IN THIS ROLE, YOU’LL BE EXPECTED TO:

• Design and implement sophisticated spectral processing, denoising, and feature extraction algorithms for low-SNR NMR data.
• Develop robust baseline correction, peak detection, and deconvolution pipelines for in vivo spectroscopy.
• Optimize algorithms for both offline research and real-time, embedded execution.
• Lead the design of scalable data pipelines for large-scale training and validation on cloud platforms (AWS/GCP/Azure).
• Integrate DSP pre-processing stages into ML workflows for both classical and deep learning models.
• Oversee model lifecycle management, from data ingestion to deployment.
• Optimize and deploy ML models on embedded hardware for low-latency, real-time inference.
• Collaborate with hardware engineers to ensure computational feasibility and accuracy in device-constrained environments.
• Work closely with NMR physicists, AI scientists, and embedded engineers to integrate signal processing, ML models, and pulse sequence design.
• Translate complex DSP and ML concepts into actionable engineering requirements.
• Mentor junior DSP/AI engineers and guide the technical roadmap for DSP and AI integration.
• Occasionally assist in the hiring process by participating in interviews, reviewing resumes, or offering feedback.

YOU MIGHT BE RIGHT FOR THIS ROLE IF YOU HAVE:

• A Master’s or PhD in Electrical Engineering, Physics, Biomedical Engineering, Computer Engineering, or related field.
• 7-10+ years of experience in DSP, with hands-on work in spectral analysis, denoising, and feature extraction.
• Demonstrated experience with low-SNR data, ideally in NMR/MRS/MRI or related spectroscopy/imaging modalities.
• A proven track record of building large-scale, cloud-based ML training pipelines.
• Strong proficiency in Python and C/C++ (for performance-critical DSP and embedded work).
• An understanding of inverse problems, compressed sensing, or Bayesian approaches
• Familiarity with PyTorch or TensorFlow for deep learning model development.
• Experience deploying ML/DSP algorithms to embedded or edge devices.
• A strong understanding of numerical stability, computational optimization, and algorithm scalability.
• Familiarity with real-time medical device constraints and regulatory considerations.