Founding ML Engineer (Health AI, Real-Time Signals)

a16z-speedrun; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Electrokare is hiring an applied ML researcher to design, train, and improve foundation models for human physiology. The role sits at the intersection of signal processing, data science, and deep learning, working closely with the CTO to evolve models already in production.

This founding-level position balances 50% applied research with 50% production ML, focusing on training, validation, and deployment in real systems.

Qualifications

• MS or PhD in machine learning, computer science, electrical engineering, applied math, biomedical engineering, or related field.
• 3–7 years of experience in ML or related roles.
• Experience working on physiological signals, biosignals, medical ML, or similar domains is a strong plus.

Responsibilities

• Design, train, and improve ML and deep-learning models.
• Work with large proprietary physiological datasets and real-world signal noise.
• Apply signal processing techniques alongside modern deep-learning approaches.
• Read, evaluate, and implement ideas from current scientific literature.
• Move models from research to production (training - validation - deployment).
• Collaborate with the CTO on model architecture, evaluation, and roadmap.
• Build tooling for experimentation, validation, and performance tracking.
• Communicate results clearly through metrics, visualizations, and reports.

Skills

Deep learning

Time-series analysis

Data science fundamentals

Python programming

Education

MS or PhD in ML/CS/EE/Applied Math/BME

Tools

Docker

Kubernetes

GCP

Azure

SQL

MongoDB

Bigtable

Job description

Electrokare is hiring an applied ML researcher to design, train, and improve foundation models for human physiology. The role sits at the intersection of signal processing, data science, and deep learning, working closely with the CTO to evolve models already in production.

This founding-level position balances 50% applied research with 50% production ML, focusing on training, validation, and deployment in real systems.