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Machine Learning Software Engineer
2 months ago
We're seeking a skilled software engineer to join our team at RBC Capital Markets. As a Machine Learning Software Engineer, you'll be involved in a project end-to-end, from data pre-processing to implementing machine learning algorithms and front-end development.
You'll be working with a team that collaborates with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support ongoing development in areas such as reinforcement learning, unsupervised learning, and computer vision.
Your Responsibilities- Design and develop machine learning-based software solutions for solving complex problems;
- Collaborate with research and business teams to converge on the best solutions;
- Optimize algorithms and prototypical solutions for efficient implementation;
- Extend prototypes into fully functional, polished solutions ready for internal and/or external use;
- Support projects with thorough documentation of usage, design decisions, and capabilities;
- Extract, transform, and load massive datasets using distributed computing framework technologies (Hadoop, Spark, etc.).
- Bachelor's or master's degree in Computer Science, Computer Engineering, Software Engineering, or equivalent;
- Experience with software development, including co-op and internships;
- Proficiency in writing software in one of the major languages such as C++, C#, Java, Python;
- Familiarity with the Unix command line and bash scripting;
- Experience with Deep Learning packages such as TensorFlow, Theano, Keras, and PyTorch is an asset;
- Exposure to distributed computing frameworks (e.g., Hadoop, Spark) as well as SQL, NoSQL, and graph databases is an asset.
RBC Capital Markets is a leading financial institution that provides a wide range of financial products and services to institutional clients. We're committed to building inclusive teams and an equitable workplace for our employees to bring their true selves to work.
We're taking actions to tackle issues of inequity and systemic bias to support our diverse talent, clients, and communities. We also strive to provide an accessible candidate experience for our prospective employees with different abilities.
