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- Tasks: Design and implement algorithms for physiological monitoring and alert systems.
- Company: Join Biostream, a leader in health tech innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact on health monitoring with cutting-edge AI technology.
- Qualifications: MSc or PhD in relevant fields and strong Python programming skills.
- Other info: Collaborative environment with a focus on real-world applications.
The predicted salary is between 60000 - 80000 £ per year.
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.
Company Biostream
Experience Senior (5+ years of experience)
AI Scientist / Machine Learning Engineer employer: NLP PEOPLE
Contact Detail:
NLP PEOPLE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Scientist / Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to professionals in the AI and machine learning space, especially those who work at Biostream or similar companies. Attend meetups, webinars, or conferences to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving physiological data or real-time systems. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding of algorithms. Practice common machine learning problems and be ready to discuss your thought process and approach to solving them.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Biostream.
We think you need these skills to ace AI Scientist / Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Scientist/Machine Learning Engineer role. Highlight your experience with machine learning models, especially those related to time-series or sensor data. We want to see how your skills align with our needs!
Showcase Your Projects: Include any relevant projects you've worked on, particularly those involving physiological signal processing or Bayesian models. This gives us a glimpse of your hands-on experience and how you tackle real-world problems.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're passionate about this role and how your background makes you a great fit. Don't forget to mention your programming skills in Python and any experience with frameworks like PyTorch or TensorFlow!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at NLP PEOPLE
✨Know Your Algorithms
Make sure you brush up on the algorithms relevant to physiological signal analysis and risk detection. Be ready to discuss how you would implement Bayesian models and probabilistic frameworks in real-world scenarios, as this will show your understanding of the role's core responsibilities.
✨Showcase Your Programming Skills
Since strong programming skills in Python are a must, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot or discuss your previous projects involving machine learning frameworks like PyTorch or TensorFlow. Have examples ready!
✨Familiarise with Real-World Applications
Understand how machine learning is applied in healthtech and clinical datasets. Be prepared to discuss any experience you have with wearables or biosensor data, as this could set you apart from other candidates. Show them you can connect theory with practice!
✨Prepare for Technical Questions
Expect questions about handling noisy or incomplete datasets and how you evaluate model performance. Think through your past experiences and be ready to explain your approach to building robust machine learning systems, especially in complex environments.