Principal Machine Learning Engineer – AQMed

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About the Role

The AQMed team is seeking a Principal Machine Learning Engineer (MLE) with deep expertise and industry experience to tackle complex challenges in advancing cardiac diagnostics. This role will add a critical perspective in identifying optimal approaches to working with data. The Principal MLE will not only be able to work with rich data sets (both synthetic and real clinical study data) but will also work at the cutting edge of architecting, training, and productizing Deep Learning (DL) and Machine Learning (ML) models. This is an opportunity to bring your expertise to a small but dynamic team that thrives on research, iteration, and collaboration. You will also work closely with external partners to refine and advance groundbreaking technology that has the potential to transform patient care. We’re always on a quest to improve and gain new insights as we continuously engage with external partners.

What You’ll Do

  • Hone your technical leadership by applying hard-won practical experience in deep learning to tackle challenging yet rewarding problems at the forefront of medical science, while collaborating with a world-class team.
  • Design, train, and fine-tune state-of-the-art deep learning architectures that seamlessly integrate multimodal data for robust and scalable solutions.
  • Harness cutting-edge AI techniques to uncover novel biomarkers, combining insights from the latest research, experimental data, and explainability frameworks to drive groundbreaking clinical applications.
  • Contribute to building and refining pipelines that power a sophisticated custom AI stack, delivering precise predictions, actionable insights, and user-friendly visualizations tailored for medical and scientific innovation.
  • Develop tools for explainable AI and integrate findings into production systems.
  • Build dashboards and tools to communicate progress and performance using AWS and other 3rd party tools.
  • Adeptly present insights to cross-functional teams as well as internal and external stakeholders, targeting your communication to the particular audience and anticipating the questions and proof points that speak best to their perspective.
  • Mentor junior team members, fostering a collaborative and innovative team environment with people management potential in the future.

About You

  • Experience: 10+ years as a data scientist or MLE, including experience working with time series data, biomedical models (especially cardiac), and training AI models on very large datasets. Masters or PhD in computer science, data science, mathematics, physics, AI or a related field.
  • Achievements: You’ve developed and deployed state-of-the-art deep learning models to production for real-world applications, especially in MedTech. You’ve shipped products, bringing practical, seasoned knowledge to navigate challenges and deliver results.
  • Deep Learning and Machine Learning Expertise: Proficient in advanced DL/ML frameworks, with a focus on self-supervised methods like contrastive learning, generative modeling, masked modeling, graph networks, and clustering. Skilled in hyperparameter tuning, explainability, and transfer learning. Bonus points for experience training large models on GPUs, working with LLMs, and using synthetic data to augment small real-world datasets.
  • Engineering capabilities: You’re proficient in Python and using versioning control systems (e.g., git), show high-quality code standards, and actively collaborate with other scientists and researchers reviewing PRs and writing scalable, maintainable code. 
  • Statistical Expertise: You have a strong foundation in statistics, probability, and data wrangling from varied sources.
  • Cloud Proficiency: Comfortable with AWS, docker, and scalable batching pipelines.
  • Cross-functional Collaboration: Skilled at bridging technical, clinical, and business perspectives by presenting insights tailored to diverse stakeholders. Builds intuitive dashboards and visualization tools to drive shared understanding, alignment, and actionable decisions across teams.
  • Mindset: You thrive in ambiguity, take ownership, balance competing priorities effectively, and excel at translating a compelling vision into technical reality.

The US base salary range for this full-time position is expected to be $225k-368k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.

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