Machine Learning Engineer - Modeling Focus

Machine Learning Engineer - Modeling Focus

At Sleepiz, we are shaping the future of healthcare to ensure that patient care is more personalized, comfortable, and supportive by utilizing real-world data collected from patients in their Homes.

We are seeking a talented Machine Learning Engineer to join our team at Sleepiz and drive innovation in real-time vital signs monitoring. You will play a key role in developing our machine learning algorithms for performance, scalability, and real-time application. This position involves a mix of model training and optimization, system adaptation for real-time analysis, and reducing resource needs for inference.

Your contributions will directly impact the scalability and efficiency of Sleepiz's cutting-edge health monitoring solutions, ensuring high-quality, real-time insights for patients and healthcare providers.

Key Responsibilities:

• Train/Optimize Machine Learning Models: Analyze and refine existing machine learning models for real-time vital signs monitoring, employing techniques like pruning, quantization, and knowledge distillation to enhance efficiency while maintaining accuracy.
• Rewrite Performance-Critical Code: Identify computational bottlenecks in current algorithms and rewrite resource-intensive operations using high-performance languages like Rust.
• Adapt Algorithms for Real-Time Processing: Ensure optimized models are adapted for real-time computation, enabling efficient live data processing for continuous health monitoring.
• Collaborate Across Teams: Work closely with software engineers, data scientists, and healthcare experts to ensure seamless integration of optimized algorithms into the Sleepiz platform.
• Contribute to Scalability and Reliability: Implement and test solutions that enhance the scalability, reliability, and robustness of Sleepiz's vital signs monitoring system.
• Stay Updated: Keep up with the latest developments in machine learning optimization and high-performance computing to continually improve our solutions.

Key Requirements:

• Strong understanding of machine learning principles and experience optimizing models for performance.
• Proficiency in Python.
• Familiarity with model optimization techniques, including pruning, quantization, and knowledge distillation.
• Experience adapting algorithms for real-time data processing.
• Strong problem-solving and analytical skills to identify and resolve algorithmic inefficiencies.
• Understanding of performance-critical coding principles and experience rewriting computationally intensive code.
• Knowledge of software engineering best practices, including testing, debugging, and version control using Git.

Nice To Have

• Exposure to MLOps workflows and deploying machine learning models in production environments.
• Experience with NN architectures for sequential data (e.g., transformers, RNNs).
• Experience with the Rust programming language.
• Familiarity with healthcare or biometric data systems.
• Experience working with live data streams or similar real-time systems.
• Knowledge of system scalability and distributed computing principles.