AI/ML Engineer

About the role

Tiposi is a medical device startup located in Silicon Valley, focused on creating AI-driven, microwave-based brain imaging technology aimed at stroke detection and enhancing global access to brain health assessments. We utilize radio frequency innovations, custom ASICs, and machine learning to improve the speed, safety, and accessibility of imaging.

As an AI/ML Engineer, you will contribute to the development of a medical imaging device that reconstructs images from challenging sensor data. This position emphasizes the application of machine learning within real-world systems rather than theoretical research.

Key facts

What you'll do

• Create and train multi-task machine learning models for stroke detection using features derived from radio frequency data, including both binary and multi-class classification, along with auxiliary prediction tasks.
• Develop and maintain high-quality ML code for transforming signals into images.
• Design and assess ML methods for solving inverse problems while managing noise and limited data availability.
• Integrate machine learning models with current signal processing and hardware systems.
• Troubleshoot issues related to training failures, data inconsistencies, and edge cases.
• Balance model complexity, reliability, and interpretability in your work.
• Work collaboratively with engineers specializing in hardware, digital signal processing, and software.

Requirements

• A Master's degree in Computer Science, Electrical Engineering, or a related discipline, or a minimum of 5 years of relevant industry experience in machine learning.
• Proficiency with generative models such as diffusion models, variational autoencoders, or encoder-decoder architectures applied to 2D or 3D data.
• Experience in constructing and managing machine learning pipelines beyond simple notebook environments.
• Comfort in handling real-world data that is often imperfect and noisy.
• Strong understanding of linear algebra, probability, and optimization principles.
• Capability to consider system-level constraints in addition to model performance.

Nice to have

• Background in signal or image processing, especially in areas like radar, microwave, or compressed sensing techniques.
• Familiarity with multimodal models or cross-modal generation techniques.
• Previous experience optimizing models for deployment on GPUs or edge devices.
• Knowledge of medical device regulatory standards and the application of AI in FDA-regulated settings.
• Experience working in medical device or healthcare technology sectors.

Skills & tools

Experience with one or more of the following in practical applications:

• Inverse problems and reconstruction techniques
• Generative or probabilistic models (e.g., VAEs, diffusion models, GANs)
• Convolutional neural networks or learned image reconstruction methods
• Physics-informed approaches that combine signal processing and machine learning
• Techniques for uncertainty estimation and enhancing model robustness

Practical notes

This position begins with a contract phase lasting 3 to 6 months, offering a monthly compensation between $4,000 and $8,000 to assess mutual fit. After transitioning to a full-time role, the salary will range from $100,000 to $120,000, supplemented by equity, depending on experience. Full-time employees will receive benefits that include health insurance (medical, dental, vision) and performance-based bonuses.