Fullscript - Machine Learning Engineer
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Requirements
• 3+ years of experience building and implementing LLM applications used in production, including conversational or agent-based systems • Experience with LLM application frameworks (LangChain, LangGraph, Hugging Face tools), including agent orchestration, tool use, or RAG pipelines • Familiarity with model evaluation and monitoring frameworks for LLM outputs and conversational quality • Knowledge of MCP and agent orchestration tools • Strong proficiency in Python and SQL • Deep understanding of data engineering best practices, including version control, testing, and CI/CD methodologiesgineering best practices, including version control, testing, and CI/CD m • Experience designing AI systems that answer open-ended questions, support follow-up interactions, and explain their reasoning • Ability to communicate complex technical concepts effectively to technical and non-technical stakeholders • Learning mindset, with a keen interest in exploring new technologies and methodologies • Familiarity with model evaluation and monitoring frameworks • Snowflake Snowpark or PySpark experience • Knowledge of MCP, Langfuse, or agent orchestration tools • Exposure to healthcare, clinical data, or decision-support systems • What we can offer you: • SalaryFlexible PTO & competitive pay—rest fuels performance. • RRSP match & stock options—invest in your future. • Customizable benefits—flexible coverage, paramedical services, and an HSA. • Fullscript discounts—save on wellness products. • Continuous learning—training budget + company-wide initiatives. • Wherever You Work Well—hybrid and remote flexibility. • $140,000 - $155,000 a year • Fullscript shares salary ranges to support transparency and help candidates make informed decisions. The range shown reflects base salary only and does not include stock options, wellness stipends, or other benefits that are part of Fullscript’s total rewards package. • Final compensation depends on experience, skills, and location. We review pay regularly to stay aligned with market data and internal equity. Benefits and total rewards may vary by region. • Great work happens when people feel supported, trusted, and inspired. At Fullscript, we stay curious and keep finding smarter ways to make care better. We grow together, take on new challenges, and focus on impact. We put people first, work as a team, and leave egos at the door. • We’re grateful for the interest in joining Fullscript. To make sure your application reaches our hiring team, please apply directly through our careers page. We’re not able to respond to individual messages about open roles on email or social channels.
Responsibilities
• Develop and implement machine learning models to solve business problems within the company's scope of work in Toronto, Ottawa, or Calgary areas. • Collaborate with cross-functional teams including data scientists, engineers, product managers, and other stakeholders throughout the project lifecycle from requirements gathering through model deployment to monitoring performance post-deployment. • Perform exploratory data analysis (EDA) on large datasets using tools like Python or R for insights into business problems; identify patterns that can be used as a basis for building predictive models, and validate these findings with domain experts when necessary. • Design, develop, train, optimize, evaluate, test, debug, document ML solutions to ensure they meet the company's performance standards while adhering to best practices in data science methodology; maintain an understanding of evolving technologies relevant to machine learning and stay abreast with industry trends through continuous education. • Create custom scripts or pipelines for automating repetitive tasks, such as preprocessing raw datasets into cleaned formats suitable for model training using tools like Pandas in Python. • Monitor ML models' performance over time; identify potential issues related to data drift and retrain the models accordingly when necessary with an eye towards maintaining high accuracy levels while minimizing costs associated with manual intervention or human error during this process. • Communicate technical concepts clearly across different audiences including non-technical team members, business stakeholders, senior management; translate complex ideas into actionable insights that drive decision making within the organization's broader strategic objectives while ensuring compliance with relevant regulations governing data privacy and security policies. • Participate in code reviews as part of a team effort towards maintaining high standards for quality assurance; provide constructive feedback on peers’ work when appropriate based upon established coding conventions within the organization's culture around software development best practices such as PEP 8 style guide or SOLID principles. • Attend regular meetings with cross-functional teams including data scientists, engineers, product managers and other stakeholders; actively contribute ideas for improving processes related to machine learning workflows within the company's scope of work in Toronto, Ottawa, or Calgary areas while ensuring alignment between technical solutions proposed by team members across different disciplines towards common business goals. • Collaborate closely with senior management and other key decision makers throughout all stages from requirements gathering through model deployment to monitoring performance post-deployment; provide regular updates on progress made during each phase along with recommendations for next steps based upon feedback received thus far within the organization's broader strategic objectives while ens0 • Machine Learning Engineer - Toronto, ON / Ottawa, ON / Calgary, AB