AI/ML Engineer

Tiposi; Milpitas (CA); On-site; USD 100,000 - 120,000; Full time

Job summary

Tiposi, a Silicon Valley medical device startup, seeks a machine learning engineer to advance applied ML embedded in real systems for brain imaging. You will design, implement, and maintain ML pipelines that integrate with signal processing hardware and clinical constraints.

Responsibilities include developing multi-task ML models for stroke detection, building production-quality code for signal→image reconstruction, and collaborating with hardware and software engineers.

Qualifications

• MS in CS, EE, or related field or 5+ years of industry ML experience.
• Experience with generative models (diffusion models, VAEs, or encoder-decoder architectures) applied to 2D/3D data.
• Experience building and maintaining ML pipelines beyond notebooks.
• Comfort working with imperfect, noisy, real-world data.
• Solid foundations in linear algebra, probability, and optimization.
• Ability to reason about system-level constraints, not just model performance.

Responsibilities

• Develop and train multi-task ML models for stroke detection using RF-derived features.
• Build and own production-quality ML code for signal → image reconstruction.
• Design and evaluate ML approaches for inverse problems under noise and data scarcity.
• Integrate ML models with existing signal processing and hardware pipelines.
• Debug training failures, data issues, and edge cases.
• Make tradeoffs between model complexity, robustness, and interpretability.
• Collaborate closely with hardware, DSP, and software engineers.

Skills

Generative models

ML pipelines

System-level thinking

Linear algebra

Probability

Optimization

Edge deployment

Cross-functional collaboration

Education

MS in CS/EE or related field

5+ years ML experience

Job description

Tiposi is a Silicon Valley medical device startup developing AI-powered, microwave-based brain imaging technology — built to detect strokes and expand access to brain health screening worldwide. We combine RF innovation, custom ASICs, and machine learning to make imaging faster, safer, and more accessible than ever before.

We are building a medical imaging device that reconstructs images from noisy, physics-constrained sensor data. This role focuses on applied machine learning embedded in real systems, not standalone research models.

You will design, implement, and maintain ML pipelines that integrate with signal processing, hardware, and clinical constraints.

Responsibilities

• Develop and train multi-task ML models for stroke detection using RF-derived features, including binary and multi-class classification as well as auxiliary prediction tasks.
• Build and own production-quality ML code for signal → image reconstruction
• Design and evaluate ML approaches for inverse problems under noise and data scarcity
• Integrate ML models with existing signal processing and hardware pipelines
• Debug training failures, data issues, and edge cases
• Make tradeoffs between model complexity, robustness, and interpretability
• Collaborate closely with hardware, DSP, and software engineers

Required

• M.S. in Computer Science, Electrical Engineering, or related field, or 5+ years of industry experience in machine learning
• Experience with generative models (diffusion models, VAEs, or encoder-decoder architectures) applied to 2D/3D data
• Experience building and maintaining ML pipelines beyond notebooks
• Comfort working with imperfect, noisy, real-world data
• Solid foundations in linear algebra, probability, and optimization
• Ability to reason about system-level constraints, not just model performance

Preferred Qualifications

• Signal or image processing background, particularly with radar, microwave, or compressed sensing techniques
• Experience with multimodal models or cross-modal generation
• Prior work optimizing models for GPU or edge deployment
• Familiarity with medical device regulatory standards and a clear understanding of the application of AI technologies within FDA-regulated environments.
• Experience in medical device or healthcare technology companies

Experience with one or more of the following in applied contexts:

• Inverse problems / reconstruction
• Generative or probabilistic models (e.g., VAEs, diffusion, GANs)
• CNNs or learned image reconstruction
• Physics-informed or hybrid signal-processing + ML approaches
• Uncertainty estimation and robustness

We care about how you choose models, not whether you’ve memorized architectures.

Compensation Structure

This role begins with a 3-6 month contract phase at $4,000-$8,000/month to establish mutual fit. Upon conversion to full-time employment, salary range is $100,000-$120,000 plus equity, based on experience. Full-time benefits include health insurance (medical, dental, vision) and performance bonuses.