At Aquatic, we are actively recruiting for a Software Engineer experienced in developing, managing, and maintaining Machine Learning systems in production. In this role, you will partner with the Quantitative Research team to build battle-tested production trading systems and create automated infrastructure to accelerate delivery of new models to live trading.

Role Details

• Develop and manage high quality, robust and efficient data and model pipelines
• Build tools that bring our models to live trading efficiently and in an automated, reproducible fashion
• Improve capabilities, performance, reliability, scalability and throughput of trading systems
• Work closely with quantitative, portfolio researchers to improve the profitability of trading tactics

Technical Experience: 

• At least 3+ years of full-time professional software development experience
• Previous experience with building and maintaining production machine learning systems and/or real-time data ingestion
• Strong background in software engineering
• Expertise in Python and/or C++
• Comfortable with iterative software development; high standards for software quality and hygiene
• No experience required in finance and/or quantitative trading; interest is beneficial

Candidate Qualities:  

• Strong bias for action
• Driven by accountability and internal urgency
• Desire to independently seek best solutions
• Preference for working in a team that focuses on delivering results aligned with Research goals
• Comfortable providing and receiving actionable feedback in a collaborative team setting
• Motivated by an ambitious environment and driven colleagues 

The base salary for this role is anticipated to be between $150,000 and $300,000, which is based on information at the time of posting. This position may also be eligible for additional forms of compensation, such as a discretionary bonus, and benefits. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their unique skills, qualifications and relevant experience.