Machine Learning Engineer, auryx

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Information Engineering, Biomedical Engineering, or a related field
• 3+ years of work experience as an ML / AI Engineer or similar role
• Solid Python skills and hands-on experience with ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.)
• Experience building and maintaining ML pipelines in production
• Proven ability to write clean, maintainable, and well-tested code in a collaborative environment
• Curiosity, willingness to learn, and flexibility to adapt and grow in a fast-moving, uncertain startup environment
• Comfortable working in small, fast-moving teams, and able to take ownership and independently drive projects

Desirable

• As a machine learning engineer at auryx, you will focus on developing ML models for health and biosignal monitoring, working with audio and physiological time series data. You'll transform sensor data into actionable health metrics, and contribute to developing new physiology-based features as our technology and product evolve. You will also contribute to early architecture decisions around data pipelines, model deployment, and MLOps, and spend much of your time on rapid prototyping, experimentation, and iteration based on feedback and results
• As an early employee, you will have significant potential to bring ownership and direction to our technical work while contributing across the breadth of startup activities. Beyond core ML development, you might find yourself contributing to product decisions, supporting data collection and validation efforts, or helping shape our approach to health feature development and user testing
• You will work closely with the CTO on a day to day basis, as well as our team of ML engineers, collaborating on the design, development and deployment of our ML system. The position offers the full spectrum of early-stage startup experience, from prototyping and technical problem-solving to strategic planning and cross-functional collaboration, giving you exposure to both complex technical challenges and the decision-making that defines our product roadmap and company trajectory

• 1. Apply with your profile and a brief description of a model/system you’ve trained and deployed end-to-end
• 2. Initial screening with the CTO
• 3. Technical interview
• 4. Culture fit interview