AI Scientist / Machine Learning Engineer

Biostream; Greater London; On-site; GBP 60,000 - 85,000; Full time

Job summary

A leading technology firm in Greater London seeks an experienced AI Scientist or Machine Learning Engineer. This role involves designing and implementing algorithms for physiological monitoring systems, focusing on robust machine learning solutions capable of detecting physiological deterioration. Candidates should have an MSc or PhD in a relevant field and strong programming skills in Python. Familiarity with frameworks like PyTorch and TensorFlow is essential. The position offers an exciting opportunity to work in healthtech and contribute to impactful technology.

Qualifications

• Strong experience developing machine learning models for time-series or sensor data.
• Ability to work with imperfect, noisy, or incomplete datasets.
• Experience building data pipelines and training workflows.

Responsibilities

• 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.
• Build pipelines for sensor data processing, feature extraction, and model training.

Skills

Machine learning model development

Python programming

Time-series data processing

Education

MSc or PhD in machine learning, AI, computer science, applied mathematics

Tools

PyTorch

TensorFlow

JAX

Job description

AI Scientist / Machine Learning Engineer

Role

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.

Responsibilities

• 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

Profile

• 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, TensorFlow, or JAX
• Experience building data pipelines and training workflows
• Ability to work with imperfect, noisy, or incomplete datasets

Nice to Have

• 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

What You Will Work On

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