The RoleThis is a core applied ML role focused on building and improving our foundation models.
You will sit at the intersection of signal processing, data science, and deep learning, working directly with the CTO to develop models that are already live and evolving toward even better performance. We see this as a founding level role with opportunity to own the AI work.
The role is roughly 50% applied research and 50% production ML. You'll read and dissect papers, design experiments, train and evaluate models, and then deploy them into real systems.
What You'll Do- Design, train, and improve ML and deep-learning models
- Work with large proprietary physiological datasets and real-world signal noise
- Apply signal processing techniques alongside modern deep-learning approaches
- Read, evaluate, and implement ideas from current scientific literature
- Move models from research to production (training 1 validation 1 deployment)
- Collaborate closely with the CTO on model architecture, evaluation, and roadmap
- Build tooling for experimentation, validation, and performance tracking
- Communicate results clearly through metrics, visualizations, and reports
Technical Requirements:Machine Learning & Data
- Deep learning with TensorFlow/Keras and/or PyTorch
- Strong foundation in signal processing and time-series analysis
- Solid data science fundamentals and statistical reasoning
Programming
- Proficiency in Python
- Experience with one or more of: C++, Rust, R, C
- Ability to read and work across multiple languages as needed
Infrastructure & Deployment
- Model deployment using Docker and Kubernetes
- Cloud experience (GCP and/or Azure)
- Working knowledge of databases (SQL, MongoDB, Bigtable, etc.)
Communication
- Clear data visualization and reporting
- Ability to explain complex models and results to technical and non-technical teammates
AI Tools
- Comfortable using modern AI-assisted development tools (e.g., Claude Code, CodeX, similar)
- Uses tools to move faster, not to replace judgment
Research- Strong ability to read, understand, and critically evaluate scientific papers
- Experience implementing methods from literature, not just using libraries
- Background in applied research, with publications strongly preferred
Ideal Background- MS or PhD in machine learning, computer science, electrical engineering, applied math, biomedical engineering, or related field
- 3-7 years of experience
- Prior work on physiological signals, biosignals, medical ML, or similar domains is a strong plus
- Experience working in environments where models are deployed and used, not just published
Culture Fit- Low ego, high ownership
- You like being useful
- You care about doing things well, even when no one's watching
- You're comfortable jumping in wherever needed
- You care about correctness, not just accuracy metrics
- You're comfortable working with messy real-world data
- You're curious and skeptical in the right ways
- You want your models to matter in practice
- You work VERY hard
