ML-Scientist at Limbic

 

Limbic is making personalized mental healthcare available to everyone, everywhere. Using artificial intelligence, we've built a virtual therapy assistant to support patients and clinicians throughout the care pathway. Our innovative AI, in combination with the unique data we are collecting, has proven to be our key differentiator.

We're growing quickly, venture-backed, and supported by Innovate UK, the European Commission, and the NHS Academic Health Sciences Network. Our software is already used in over 40% of NHS talk therapy services, and we are starting to explore new opportunities in other care systems.

It’s challenging. We’re ambitious. And we’re helping one another achieve our best and make a real impact. 

 

About the role

As a Machine Learning Scientist, you will work closely with other members of the Research and Product Teams to improve what our AI systems can do in a clinical setting, often working as pioneers on unsolved problems requiring a high degree of creativity. This work will often involve leveraging our large datasets (300k patient profiles and counting) to build novel ML models that improve clinical accuracy, safety, and patient experience. More often than not, this work will involve using classic ML techniques like tree-based models in combination with large language models.

At Limbic, we strongly believe that using domain-specific models, for example, a model that detects cognitive distortions, is what ultimately enables the success of LLM-based systems. That’s why you might be an excellent candidate if you have domain expertise in psychology and psychiatry, which you know how to translate into testable hypotheses and ML models using real-world data.

You should apply if you are passionate about using your strong analytical and technical skills, perhaps coming from a clinical background, to build scalable solutions that improve the lives of thousands of patients every month.

 

Responsibilities

• Continuously improve the clinical capabilities of our product suite using tools from machine learning
• Build new LLM-based features that improve patient and clinician experience of our products
• Work to ensure the safety of our products using classifiers, filtering, prompting, and finetuning of LLMs
• Collaborate with other members of the Research Team to improve our tech stack
• Work with the team to write patents or publish high-impact papers about our cutting-edge research

 

Requirements

Essential:

• MSc or higher in Psychology, Computational Psychiatry, Neuroscience, Cognitive Science, Computer Science, Machine Learning, or other related disciplines with a strong focus on quantitative skills
• Strong data analysis skills in Python or R
• Strong programming skills in Python
• Prior experience training and analyzing Machine Learning models, e.g. neural networks, transformer architectures, XGBoost models, Bayesian networks, etc 
• The ability to prototype fast to test ideas quickly 
• The ability to reason with data and share insights with relevant stakeholders in a clear way
• Be comfortable working in a fast-paced, high-uncertainty environment
• Strong communication skills
• A strong focus on real-world impact 

 

Desirable:

• PhD or postdoctoral experience in a quantitative field, ideally Psychology, (Computational) Psychiatry, or Neuroscience
• Prior experience prompting and fine-tuning LLMs
• Prior experience working with large datasets and querying data using SQL
• Prior experience working in a startup environment

• Creative problem-solving about how LLMs and AI can improve our product

 

Benefits

• Competitive salary
• An amazing office in central London (flexibility regarding working from the office and WFH)
• Twice a year, company-wide meet-ups in Europe
• 25 days PTO
• Pension scheme
• Paid maternity, paternity, and parental leave packages
• Equity share options
• Support with purchasing work-related books and materials

 

We take employee well-being seriously at Limbic, and in addition to the above, we offer:

• Quarterly ‘life’ days (4 days per year)
• Access to mental health support
• Regular wellbeing initiatives