Machine Learning Scientist Intern

Who We Are 

We’re a mission and values driven company with tremendous dedication to our customers. Our 100% remote team, spread internationally in Canada, the US, and beyond, is dedicated to a common goal – to revolutionize healthcare through innovation, collaboration, and commitment to our core values and behaviors.  

At Synthesis Health, we are committed to transforming healthcare through innovative technology, leveraging AI/ML and scalable, native cloud architectures to build solutions that make a meaningful difference. Our engineering team is at the heart of this mission, and we are looking for individuals who are passionate about building secure, scalable, and compliant systems. 

About the Opportunity 

Synthesis Health is looking for Machine Learning Scientist students who are interested in applying their academic knowledge to solve real-world healthcare solutions. As a Machine Learning Scientist intern you will work with a dedicated team, invested in your professional development, to design, develop and deploy machine learning models.  

Key Responsibilities: 

Design and Develop ML Models. 

• Collaborates with team members to define objectives for AI systems, including GenAI and LLM-based applications. 

• Designs and develops machine learning models using structured and unstructured data (image, tabular, and text), with a strong focus on large-scale text data. 

• Selects appropriate datasets, data representation methods, and pre-trained models (e.g., GPT, LLaMA) to fine-tune or build generative applications. 

• Builds and integrates LLM-powered components, such as text summarizers, report generators, chat agents, or document classifiers. 

• Follows team procedures for documenting all steps in the GenAI development lifecycle. 

• Utilizes Gitab for version control, collaboration, and documentation of both ML and LLM workflows. 

Deploys ML Models. 

• Conducts experiments and evaluations to assess ML and LLM model quality, using both traditional metrics and domain-specific evaluations (e.g., hallucination rate, reasoning score). 

• Fine-tunes foundation models (e.g., OpenAI, Cohere, open-source LLMs) to improve performance for specific use cases. 

• Collaborates with MLOps and engineering teams to deploy and monitor GenAI pipelines using modern deployment frameworks. 

• Uses MLOps and LLMOps tools (e.g., LangSmith,  MLflow) to track experiments and optimize models 

Communication and Collaboration. 

• Participates in daily team huddles and scheduled department meetings. 

• Shares progress on GenAI/LLM-based features and flags technical risks or limitations early. 

• Works collaboratively with team members, internal and external stakeholders. 

• Participates in discussions, offers solutions, and asks questions to ensure a thorough understanding of assignments.  

Research and Knowledge Management. 

• Actively researches advances in foundation models, transformers, and GenAI architectures. 

• Keeps up with evolving tools and techniques for fine-tuning, RAG, prompt engineering, and model evaluation. 

• Presents findings and proof-of-concepts related to GenAI applications in medical, scientific, or enterprise domains. 

• Translates research into practical improvements for model development. 

 Documentation. 

• Provides the required input to the documentation process needed for compliance and regulatory purposes. 

 About the Right Candidate 

You can work in a fast-paced environment and juggle multiple projects with overlapping deadlines.  

Qualifications 

• Bachelor’s degree in a relevant field. 

• Academic or industry experience in machine learning, including supervised/unsupervised learning, deep learning, and GenAI. 

• Hands-on experience with LLMs (e.g., GPT-4, LLaMA, Claude) and familiarity with tools like LangChain, Flowise, or Hugging Face Transformers. 

• Pursuing an advanced degree with academic studies in machine learning techniques including  supervised and unsupervised learning, deep learning, reinforcement learning, etc.  

• Strong programming skills in Python. Experience with PyTorch, TensorFlow, or other relevant ML/LLM frameworks is a plus. 

• Understanding of RAG workflows, fine-tuning techniques, and prompt optimization. 

• Ability to communicate with internal and external stakeholders.  

• Strong critical thinking skills. 

You will adhere to our company’s values and behaviors and incorporate them in your interactions with colleagues and customers.  

Values:  

1. Clinical service first. 2. Collaborate with our customers. 3. Listen, respect, learn. 4. Innovate to excel. 

Behaviors: 

1. Be nice. 2. Be creative. 3. Be honest. 4. Be helpful. 

Compensation and Benefits 

Compensation is paid in the currency of the country where the individual is hired. For Canadian applicants, the salary range is $20-$25 per hour CAD.   

Other benefits include but not limited to: Medical, Dental, Vision, “Use as needed” vacation policy, and participation in our employee option program.