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About Tiposi

Tiposi is a Silicon Valley-based medical device startup dedicated to developing AI-driven, microwave-based brain imaging technology designed to detect strokes and broaden global access to brain health screening. Our innovation combines radio frequency technologies, custom ASICs, and machine learning to create imaging solutions that are faster, safer, and more accessible than traditional methods.

Role Overview

We are seeking an AI/ML Engineer to help build a cutting-edge medical imaging device that converts noisy sensor data governed by physical constraints into accurate images. This position focuses on practical machine learning applications embedded in real-world systems rather than theoretical research models. The candidate will design, develop, and maintain ML workflows that collaborate closely with signal processing, hardware components, and clinical requirements.

Primary Responsibilities

Develop and train multitask machine learning models that detect stroke by leveraging radio frequency-derived features; tasks include binary and multi-class classification along with auxiliary predictions.
Create and maintain production-ready ML software that translates signals into reconstructed images.
Design and test ML methods addressing inverse problems under conditions of noise and limited data.
Seamlessly integrate ML models with established signal processing and hardware systems.
Troubleshoot training failures, data anomalies, and edge case scenarios.
Balance considerations of model complexity, robustness, and transparency.
Work collaboratively with teams specializing in hardware, digital signal processing, and software engineering.

Mandatory Qualifications

Master's degree in Computer Science, Electrical Engineering, or a closely related discipline or a minimum of five years’ industry experience in machine learning.
Proficiency with generative model architectures such as diffusion models, variational autoencoders, or encoder-decoder frameworks applied to two- or three-dimensional data.
Experience developing and sustaining machine learning pipelines beyond exploratory notebook environments.
Ability to manage and interpret imperfect, noisy real-world datasets.
Strong understanding of linear algebra, probability theory, and optimization techniques.
Capability to evaluate system-wide constraints beyond pure model metrics.

Preferred Skills and Experience

Background in signal or image processing, especially radar, microwave, or compressed sensing technologies.
Familiarity with multimodal and cross-modal machine learning models.
Experience optimizing models for graphical processing units or edge computing devices.
Knowledge of regulatory standards applicable to medical devices and the use of AI technologies within FDA-regulated environments.
Previous work experience within medical device or healthcare technology companies.

Relevant Applied Machine Learning Experience

Working on inverse problems or image reconstruction projects.
Using generative or probabilistic models such as VAEs, diffusion models, or GANs.
Implementing convolutional neural networks or learned image reconstruction techniques.
Applying physics-informed or hybrid signal-processing and ML methods.
Developing models with uncertainty estimation and robustness in mind.

Compensation and Employment Terms

The position begins as a 3 to 6-month contractual engagement with a stipend ranging from $4,000 to $8,000 per month to ascertain mutual suitability. Upon successful conversion to a full-time role, the salary will range between $100,000 and $120,000 annually, commensurate with experience, and include equity participation. Full-time employees are entitled to health insurance covering medical, dental, and vision care, along with performance bonus opportunities.

AI/ML Engineer

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