Research Scientist Lead; Machine Learning

At Presage Technologies, we believe in improving the quality of healthcare while also making it more affordable and accessible. Over 90% of the world’s population haves access to a device that can power our software, but less than half have access to basic health services. We are aiming to close that gap by developing cutting‑edge artificial intelligence and video analytics to transform consumer electronics and mobile phone consumer apps into sophisticated health sensing platforms.

As our Research Scientist Lead, you’ll drive the technical strategy and execution of our Machine Learning and Data teams. You’ll oversee the development of sophisticated deep learning models, classical machine learning systems, and hybrid approaches. You’ll balance research innovation with production delivery, mentoring and managing engineers and scientists while solving high‑impact problems that require creative algorithmic solutions and deep domain understanding — including translating physiological principles into robust computational models.

Key responsibilities:

• Serve as a hands‑on technical research leader, setting the scientific and technical direction for model development and data collection.
• Lead the development of deep learning (CNNs, RNNs, Transformers, attention‑based models) and classical machine learning systems, making architectural decisions that balance accuracy, efficiency, interpretability, and physiological plausibility.
• Drive physiology‑informed model development, translating insights from human biological systems (e.g., cardiovascular, respiratory, autonomic) into feature engineering, signal modeling, algorithm design, and evaluation frameworks.
• Integrate mechanistic understanding, physics‑informed methods, signal processing techniques, and classical algorithms with modern ML to build robust, generalizable, and interpretable systems.
• Define and own ML architecture strategy, experimental design, and performance evaluation to ensure models are scientifically grounded and production‑ready.
• Lead, manage, and mentor engineers and scientists while fostering a culture of rigorous research, cross‑disciplinary learning, and high engineering standards.
• Guide data strategy and infrastructure decisions for physiological and time‑series data, ensuring high‑quality datasets and scalable ML pipelines.

This is a hybrid role based in St. Louis, Missouri, with a greater in‑office presence expected initially to align closely with our laboratory and research teams. You will report directly to the Chief Technology Officer.

You’re a technical leader who excels at solving complex problems with creative algorithmic solutions. You combine deep technical expertise in machine learning with strong team leadership skills, balancing ambitious research with practical product delivery.

• 5+ years of professional experience in scientific research, with a proven track record leading teams.
• Deep expertise in deep learning architectures (CNNs, RNNs, Transformers, attention mechanisms, etc.) and classical machine learning algorithms with understanding of when and why to use each approach.
• Hands‑on experience with signal processing, feature engineering, and optimization techniques for real‑world data across different domains.
• Experience mentoring, managing, or collaborating with other ML engineers and scientists in a technical capacity.
• Strong Python skills and experience with time‑series / signal processing.
• Experience developing classical algorithms (not only deep learning).
• Familiarity with Linux/macOS, and solid Git practices.

• Experience modeling biological or physiological systems (e.g., cardiovascular, respiratory, neural, or autonomic signals).
• Experience with physiology‑informed model development, physics‑informed neural networks, or hybrid mechanistic + ML approaches.
• Experience with ML production systems, MLOps, model deployment, and monitoring best practices.
• Experience with Bayesian methods, uncertainty quantification, or probabilistic modeling.
• Publications or technical contributions in biomedical engineering, machine learning, AI/ML, or related domain‑specialized applications.
• Phase 1:
  Submit a Resume
• Phase 2:
  Introductory Interview with HR
• Phase 3:
  Take Home Performance Task
• Phase 4:
  Technical Interview with CTO and a Research Scientist
• Offer and negotiations

We move quickly; the process can be completed in under two weeks for the right candidate.

$230,000 - $250,000 per year (commensurate with experience and expertise)