The Quantitative Researcher will develop and maintain machine learning algorithms for a trading platform, implement features for a trading pipeline, troubleshoot issues, mentor junior team members, and establish coding standards in a fast-paced startup environment.
About AlphaVerse
The AlphaVerse team is responsible for putting together a best-in-class mid-frequency trading and crowdsourcing platform where anybody can contribute signals and is rewarded for their innovations. We are looking for an enthusiastic and talented Quantitative Researcher to join our team. The ideal candidate thrives in ambiguous, fast-paced environments, is excited about building products from the ground up, have strong experience and proficiency in Python and Cython, and will be responsible for developing and maintaining features and machine learning algorithms for trading.
Key Responsibilities
- Research and implement features that are consumed in a machine learning trading pipeline
- Research and implement large-scale machine learning algorithms using massive financial data
- Write clean, well-documented code with appropriate test coverage
- Assist in troubleshooting and debugging production issues
- Be on-call on a rotating basis
- Help establish engineering best practices and coding standards
- Mentor future junior team members as we scale
- Take ownership of smaller projects and features from design to deployment
Requirements
- Able to thrive in the crucible of an extremely fast-paced, demanding start-up like environment
- Able to bear immense responsibility for high-stakes, large-scale production infrastructure
- Highly proficient in machine learning, with preference towards deep-learning architectures
- High proficiency in Python and proficient in PyTorch and Polars
- Experience with SQL-like databases, such as Postgres and ClickHouse database is a plus
- Experience with Cython in a performance sensitive environment is a plus
- Some knowledge of financial markets and instruments is a plus
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