Algorithm & Machine Learning Systems Engineer

About the Role

We are looking for an engineer who combines strong Data Structures & Algorithms (DSA) fundamentals with hands-on Machine Learning skills. You will design algorithms and ML pipelines that turn raw, noisy ECG and sensor signals into reliable, clinically meaningful insights. The role sits at the intersection of signal processing, mathematics, machine learning, and efficient systems engineering.

Key Responsibilities

Algorithms & Systems

• Design, develop, and optimize algorithms for real-world engineering and product problems.
• Apply strong DSA concepts to build efficient and scalable solutions.
• Analyze complex problems and convert them into well-defined computational solutions.
• Optimize algorithms for performance, memory usage, latency, and reliability.
• Identify performance bottlenecks and improve computational efficiency.
• Design and implement software modules that interact with product, device, or data-processing systems.

Machine Learning & Data Science

• Build, train, validate, and tune machine learning models using Scikit-Learn and Python data-science tooling.
• Perform feature engineering on time-series and physiological signals (time-domain, frequency-domain, and wavelet-domain features).
• Develop classification, clustering, and anomaly-detection models for detecting patterns and abnormalities in ECG and sensor data.
• Apply sound model-evaluation practices: cross-validation, handling class imbalance, precision/recall/F1, ROC-AUC, and avoiding data leakage.
• Apply statistical methods and hypothesis-driven analysis to understand data behaviour and validate results.
• Work with the AI/ML team to move models from experimentation to efficient, production-ready implementations.

Signal Processing & Time-Series

• Develop software components for processing, analyzing, and interpreting time-series, sensor, and device-generated data.
• Apply Discrete Wavelet Transform (DWT) and related techniques for denoising, decomposition, feature extraction, and QRS/peak detection.
• Design digital filters and pre-processing pipelines to handle baseline wander, motion artifacts, and noise.
• Work with large or continuous data streams and develop efficient streaming and real-time data-processing approaches.
• Handle edge cases, noisy data, missing samples, and real-world constraints while designing solutions.

Collaboration & Engineering Practice

• Collaborate with Firmware, Hardware, Backend, AI/ML, and Product teams as required.
• Write clean, maintainable, well-tested, and production-ready code.
• Participate in technical discussions, architecture and design decisions, and code reviews.
• Contribute to product development through implementation, testing, and optimization.

Requirements & Tech Stack

Required Skills

• Strong understanding of Data Structures and Algorithms (DSA).
• Strong analytical, logical, and problem-solving abilities.
• Good understanding of algorithmic complexity, including time and space complexity.
• Hands-on programming experience in Python and/or C++ (or another strong programming language).
• Practical experience with Machine Learning using Scikit-Learn (pipelines, model selection, hyperparameter tuning, evaluation).
• Solid mathematical foundation: linear algebra, probability, statistics, calculus, and optimization.
• Understanding of Discrete Wavelet Transform (DWT) and signal-processing fundamentals (Fourier transform, filtering, sampling).
• Experience with data science on time-series data: pre-processing, feature extraction, segmentation, forecasting, or anomaly detection.
• Proficiency with NumPy, SciPy, and Pandas; familiarity with PyWavelets and Matplotlib is a plus.
• Ability to design algorithms independently rather than implementing predefined solutions.
• Ability to work with data and reason about patterns, performance, and edge cases.
• Experience building software systems or technically complex software components.
• Strong software engineering fundamentals, including debugging, testing, and version control (Git).

Preferred Skills

• Experience with time-series or continuous data streams (ECG, EEG, PPG, accelerometer, or similar).
• Experience with signal processing, data filtering, anomaly detection, or pattern detection.
• Exposure to deep learning frameworks (TensorFlow / PyTorch), such as 1D-CNN, LSTM, or Transformer models for signals.
• Experience with model optimization and deployment (ONNX, quantization, model serving, edge inference).
• Experience with sensor or device-generated data, IoT, or hardware-connected software systems.
• Experience in healthcare, medical devices, digital health, or similar product environments.
• Strong C++ programming experience, including performance-critical code.
• Exposure to numerical computing, scientific computing, or statistical methods.
• Familiarity with MLOps basics: experiment tracking, data and model versioning, reproducibility.
• Experience working in a product-based organization.

Qualifications

• Bachelor's or master's degree in Computer Science, Computer Engineering, Electronics, Electrical Engineering, Mathematics, Statistics, Data Science, or a related technical discipline.
• Candidates with strong practical experience and exceptional programming, algorithmic, and ML skills may also be considered.