Machine Learning Engineer

Found in: Jooble CA O C2 - 1 week ago

Toronto ON, Canada Boosted.ai Full time

ai is a fintech company headquartered in Toronto, Canada with offices in New York City. We provide AI tools to asset managers to augment their equity capital markets research. Boosted.ai empowers finance professionals to use AI and machine learning to sort through massive amounts of data to find stock opportunities. We are a well funded, post Series-B company - our lead investors are innovators and disruptors in financial and technology markets, including Spark Capital (Twitter, Slack, Affirm), Ten Coves Capital (Plaid, Sentieo, TouchBistro), Portage Ventures (Wealthsimple, Borrowell, Nesto), and RBC (Royal Bank of Canada).
We are seeking a talented Machine Learning Engineer to join our team. The successful candidate will be responsible for developing and implementing state-of-the-art machine learning algorithms for our investment management platform. This role requires expertise in machine learning, statistics, and data analysis, as well as a passion for innovation and a desire to drive real-world impact.
Conduct research in the use of large language models for novel applications
Conduct research in machine learning and statistical modeling, with a focus on developing novel algorithms and methodologies for investment management
Design and implement experiments to evaluate the performance of machine learning models
Work closely with the engineering team to implement machine learning models in production
Stay up-to-date with the latest developments in machine learning research and apply them to our platform
Communicate research findings and methodologies to both technical and non-technical stakeholders
Masters or PhD in computer science, statistics, or a related field, with a focus on machine learning
Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn
Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders
Experience working in the finance industry or with financial data
Familiarity with natural language processing, time-series analysis, or deep learning techniques
As such, we explicitly seek people that bring diverse life experiences, diverse educational backgrounds, diverse cultures, and diverse work experiences.

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