Applied ML Engineer for Medical Imaging & Edge Deployment

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, 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.