2027 Capital Markets, Global Equities, AI Engineer (16 Months, Co-op) at RBC in Toronto, ON

Job description

Job Description RBC Capital Markets is seeking highly skilled and motivated individuals to join our Algo Research team as AI Engineers. Our team is at the forefront of leveraging artificial intelligence and machine learning to develop innovative trade execution algorithms and advanced analytics solutions. This role presents a unique opportunity to contribute to cutting-edge research and development in quantitative finance, working alongside top-tier professionals in a dynamic and collaborative environment. We are particularly interested in candidates with strong technical expertise, a passion for data-driven problem-solving, and the ability to apply advanced AI techniques to real-world problems. Experience in finance is not required. This internship position is 16 months in duration, from May 2027 to August 2028 . Students are expected to return to school following the completion of the internship. What We’re Looking For: We are seeking candidates who demonstrate the following qualifications and attributes: Software Engineering & Programming: Expertise in Python, and knowledge of databases, scripting, Unix systems, data structures, and networking. Applied Machine Learning : Proficiency in ML techniques (e.g. Feature Engineering, Loss Function Design, Deep learning, Reinforcement Learning) and tools (e.g. PyTorch, Scikit-learn, MLflow). Data Science: Experience working with large datasets to extract insights and create visualizations using libraries like NumPy, Pandas, Seaborn, and Streamlit Probability & Statistics: Strong understanding of probability concepts and statistical methods. Communication: Skilled in conveying quantitative concepts to non-technical stakeholders. Key Responsibilities: Employ advanced machine learning techniques to address complex trading related problems. Develop and optimize AI-driven trading algorithms/strategies and incorporate them into client-facing applications. Collaborate with traders, researchers, and subject matter experts to conceptualize and implement innovative products. Analyze large datasets to derive actionable insights and create impactful visualizations. Contribute to a variety of high-impact projects spanning multiple asset classes and trading strategies. Why Join RBC Capital Markets? Be part of a forward-thinking team at the forefront of AI-driven innovation in finance. Collaborate with industry-leading experts on Canada’s largest trade floor. Gain exposure to real-world applications of artificial intelligence and machine learning in quantitative trading. Thrive in a culture that values innovation, collaboration, and continuous professional development. If you are a highly motivated individual with a strong technical background and a desire to contribute to the future of quantitative finance, we invite you to apply for this exciting opportunity. In order for your application to be considered, please upload your COVER LETTER and RESUME as ONE PDF FILE . We thank all interested candidates; however, only those selected for an interview will be contacted. We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual. Job Skills Additional Job Details Address: ROYAL BANK PLAZA, 200 BAY ST:TORONTO City: Toronto Country: Canada Work hours/week: 37.5 Employment Type: Full time Platform: CAPITAL MARKETS Job Type: Student/Coop (Fixed Term) Pay Type: Salaried Posted Date: 2026-09-14 Application Deadline: 2026-10-01 Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above Pay Range The expected pay range for this position is: $60,000.00 - $100,000.00 The pay range provided is for the primary work location referenced in the Additional Job Details section of this job posting. RBC supports pay transparency