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Machine Learning Scientist Intern at Synthesis Health. . Location: Vancouver, Remote. 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: .  . . . Clinical service first. 2. Collaborate with our customers. 3. Listen, respect, learn. 4. Innovate to excel..  . . . Behaviors:.  . . . 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..