Machine Learning Developer, GFT

What is the opportunity? Are you passionate about building real‑world machine learning solutions? RBC is looking for a Machine Learning Developer to help build and deploy ML and AI applications that solve problems in risk management. You'll work alongside experienced data scientists and ML engineers, developing models, building data pipelines, and deploying solutions that impact thousands of users across the organization. You’ll spend your time writing code, training models, building features, and collaborating with cross‑functional teams to turn business challenges into working ML systems. You will work on projects where your code directly impacts how risk is identified and managed, learning from senior team members while contributing meaningfully to real products. What will you do? Write clean, well‑tested Python code to implement machine learning models, data pipelines, and features under the guidance of senior team members. Develop and train machine learning models for risk management applications using libraries like Scikit‑learn, PyTorch, TensorFlow, or similar frameworks. Build data pipelines and preprocessing workflows to prepare datasets for model training and analysis. Implement features and contribute to production ML systems, following best practices in code quality and documentation. Run experiments to test different model architectures, hyperparameters, and approaches, measuring performance and documenting results. Collaborate with data scientists and engineers to understand requirements, design solutions, and debug issues in existing systems. Analyze model performance, identify limitations, and suggest improvements to increase accuracy and reliability. Write unit tests and validation checks to ensure models and data pipelines work correctly. Document code, experiments, and findings clearly so that your work can be understood and built upon by the team. Help deploy models to staging and production environments with guidance from senior engineers. Stay current with machine learning trends and technologies, bringing new ideas and approaches to the team. What do you need to succeed? Must have: Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or related field. Master’s degree is a plus. Strong proficiency in Python, including experience with data manipulation (Pandas, NumPy) and at least one ML framework (Scikit‑learn, PyTorch, TensorFlow, or similar). Solid understanding of machine learning fundamentals including supervised learning, unsupervised learning, model evaluation, and overfitting prevention. Experience with SQL and ability to write queries to extract and analyze data. Familiarity with Git and version control workflows. Strong problem‑solving skills and logical thinking ability. Excellent written and verbal communication skills with ability to explain technical concepts clearly. Collaborative mindset and ability to learn from more experienced team members. Attention to detail and commitment to writing clean, readable, well‑documented code. Ability to work independently on assigned tasks while seeking help when needed. Nice to have: Internship or project experience building machine learning models or working on data science projects. Experience with generative AI, large language models, or transformer‑based models. Experience with cloud platforms like OpenShift, AWS, Azure, or Google Cloud Platform. Familiarity with machine learning operations tools like MLflow or similar. Knowledge of deep learning architectures and frameworks like PyTorch or TensorFlow. Experience with web frameworks like Flask or FastAPI for deploying models. Understanding of statistical analysis and A/B testing concepts. Experience with data visualization libraries like Matplotlib, Seaborn, or Plotly. Open‑source contributions or personal projects demonstrating ML work. What's in it for you? We believe in investing in our people and helping them grow and succeed. You'll join a team that values learning, collaboration, and making a real impact. Competitive compensation and benefits including health and wellness options. Mentorship from experienced machine learning engineers and data scientists who will help you grow your skills. Exposure to real‑world machine learning challenges and the opportunity to see your work deployed in production. Access to modern tools, cloud infrastructure, and ML platforms to build and experiment. Collaborative team environment with opportunities to learn from talented engineers and data professionals. Training and learning opportunities to develop your skills in machine learning, software engineering, and risk management. Clear career progression path with opportunities to take on more complex projects and greater responsibility. The satisfaction of solving real problems and building systems that make a difference. Community and support as you launch your career in machine learning and AI. #J-18808-Ljbffr