Senior Machine Learning Engineer

About Electrokare Electrokare builds software that turns ECG signals into continuous, non-invasive physiological insight. We develop machine-learning models that estimate blood-level physiology from cardiovascular signals, enabling real-time insight without lab tests or invasive sensors. Our systems are already in production, backed by proprietary datasets, and used across high-performance and medical-adjacent settings. This is not a toy ML problem. The signals are noisy, the biology is complex, and correctness matters. The Role This is a core applied ML role focused on building and improving models that map ECG signals to blood-level physiological values. You will sit at the intersection of signal processing, data science, and deep learning, working directly with the CTO to develop models that are already live and evolving toward even better performance. The role is roughly 50% applied research and 50% production ML. You’ll read and dissect papers, design experiments, train and evaluate models, and then deploy them into real systems. What You'll Do
Design, train, and improve ML and deep-learning models for ECG-to-blood inference
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 closely 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 Technical Requirements: Machine Learning & Data
Deep learning with TensorFlow/Keras and/or PyTorch
Strong foundation in signal processing and time-series analysis
Solid data science fundamentals and statistical reasoning Programming
Proficiency in Python
Experience with one or more of: C++, Rust, R, C
Ability to read and work across multiple languages as needed Infrastructure & Deployment
Model deployment using Docker and Kubernetes
Cloud experience (GCP and/or Azure)
Working knowledge of databases (SQL, MongoDB, Bigtable, etc.) Communication
Clear data visualization and reporting
Ability to explain complex models and results to technical and non-technical teammates AI Tools
Comfortable using modern AI-assisted development tools (e.g., Claude Code, CodeX, similar)
Uses tools to move faster, not to replace judgment Research
Strong ability to read, understand, and critically evaluate scientific papers
Experience implementing methods from literature, not just using libraries
Background in applied research, with publications strongly preferred Ideal Background
MS or PhD in machine learning, computer science, electrical engineering, applied math, biomedical engineering, or related field
3-7 years of experience
Prior work on physiological signals, biosignals, medical ML, or similar domains is a strong plus
Experience working in environments where models are deployed and used, not just published Culture Fit
Low ego, high ownership
You like being useful
You care about doing things well, even when no one’s watching
You’re comfortable jumping in wherever needed
You care about correctness, not just accuracy metrics
You’re comfortable working with messy real-world data
You’re curious and skeptical in the right ways
You want your models to matter in practice
You work VERY hard