AI Scientist​/Machine Learning Engineer

AI Scientist / Machine Learning Engineer

We are looking for an AI scientist or machine learning engineer to design and implement the algorithms that power Biostream’s physiological monitoring and alert systems.

You will work closely with the biostatistics team to translate probabilistic and Bayesian models into deployable algorithms that operate on real‑world sensor data. The role focuses on building robust machine learning systems capable of detecting physiological deterioration from multi‑sensor inputs in complex and noisy environments.

You will collaborate with engineers, statisticians, and medical advisors to develop models that can operate on‑device or in distributed systems with constrained connectivity.

• Design and develop machine learning algorithms for physiological signal analysis and risk detection.
• Work closely with the biostatistician to implement Bayesian models and probabilistic frameworks.
• Develop models combining vital signs, motion data, and contextual signals.
• Build pipelines for sensor data processing, feature extraction, and model training.
• Implement algorithms suitable for real‑time and edge deployment.
• Evaluate model performance and robustness across heterogeneous datasets.
• Develop simulation and testing environments for algorithm validation.
• Contribute to the integration of models into the broader software platform.

• MSc or PhD in machine learning, artificial intelligence, computer science, applied mathematics, or a related field.
• Strong experience developing machine learning models for time‑series or sensor data.
• Strong programming skills in Python.
• Experience with machine learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Experience building data pipelines and training workflows.
• Ability to work with imperfect, noisy, or incomplete datasets.

• Experience with physiological signal processing (ECG, PPG, respiration, etc.).
• Experience working with wearables or biosensor data.
• Familiarity with Bayesian modelling or probabilistic machine learning.
• Experience deploying models on edge devices or constrained hardware.
• Experience with healthtech, medical AI, or clinical datasets.

• Physiological signal processing.
• Integration of probabilistic models with machine learning systems.
• Real‑time monitoring and alert systems.
• Deployment of models in constrained operational environments.

Biostream

Senior (5+ years of experience)