Research Scientist-Graph Models
Experience level: Mid
Tech stack used: Python, PyTorch, AWS
Primary skills we consider: Python, PyTorch, Tensorflow
Secondary skills we consider: AWS, Sklearn
Employment Type: Permanent & Full time
Remote working: Not available
Visa sponsorship: Available
Machine Medicine Technologies (MMT) uses computer vision and computational statistics to enhance the neurological assessment of patients. Our first product, KELVIN, allows motor assessments in Parkinson’s disease to be performed, recorded and used both faster and better than has ever previously been possible. KELVIN-PD is already in use at clinical sites across several countries and possesses a CE mark, being a class-I medical device. MMT aims to build the platform into a generalised tool for patient selection, surgical planning and device programming for machine brain interfacing, a revolutionary therapeutic innovation.
A fast-moving startup, with an energetic and dynamic team, looking to disrupt the MedTech industry with AI. Our employees are encouraged to take on the sort of career development opportunities that cannot be found at larger, more established, companies. We are in the highly regulated medical device industry, so our engineering team has to have an even greater focus on quality, usability and versatility. We also like to blow off steam, whether that’s playing a few rounds of table tennis in the office, or arranging an online gaming session.
- Competitive salary
- Employee equity programme
- 25 days annual leave (+UK bank holidays)
- Office location close to London Bridge (Zone 1)
You are an ambitious and capable machine learning scientist, with direct experience developing probabilistic graphical models (e.g. hierarchical Hidden Markov Models), preferably in a biomedical context. You have a strong research background, either in an academic or industrial lab. You are conversant with the relevant branches of mathematical statistics. You care about impact and want to do work that matters.
List of main duties and responsibilities:
- Applying probabilistic graphical models to measure biomarkers
- Conducting research and authoring academic papers
- Contributing to our research strategy
- Keeping up to date with relevant research
- Strong understanding of probabilistic graphical models (eg. HMMs)
- Demonstrable experience applying probabilistic graphical models to real world problems
- BSc, MSc in relevant quantitative subject (statistics, mathematical engineering, etc)
- Good communication and data visualization skills
- Experience with core Python data science packages (numpy, pandas, sklearn)
- Publications at top ML conferences/journals
- PhD (ideally in probabilistic graphical models)
- Previous experience in the biomedical sector
- Experience with at least one deep learning framework (PyTorch or TensorFlow)
- Familiarity with git, bash and coding best practices.
- Familiarity with cloud infrastructure (AWS EC2, S3, Batch)
Job reference no. RSGM504