Machine Learning Engineer

AAA Global; Boston (MA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

AAA Global's Boston-based quant research and trading team seeks a Machine Learning Engineer to build and deploy predictive models powering real-time trading strategies. You will collaborate with researchers and developers to turn experiments into production-ready systems.

You will develop ML models for alpha generation, construct scalable data pipelines, and work with quants to optimize models for speed and reliability in production environments.

Qualifications

• 3–6 years of experience in ML, quant research, or software engineering.
• Strong grasp of statistics, optimization, and time-series modeling.

Responsibilities

• Develop and deploy ML models for alpha generation, risk modeling, and market forecasting.
• Build scalable data pipelines and backtesting frameworks for large financial datasets.
• Partner with quants to optimize research models for production speed and reliability.
• Monitor model performance and refine for accuracy and stability.

Skills

Machine Learning

Quant research

Software engineering

Statistics

Time-series modeling

Tools

Backtesting frameworks

Data pipelines

Production systems

Job description

Machine Learning Engineer | Boston, MA (Hybrid)

We’re looking for a Machine Learning Engineer to join a Boston-based quant research and trading team, building and deploying predictive models that power real-time trading strategies. You’ll collaborate with researchers and developers to turn experimental ideas into robust, production-ready systems.

Responsibilities:

• Develop and deploy ML models for alpha generation, risk modeling, and market forecasting.
• Build scalable data pipelines and backtesting frameworks for large financial datasets.
• Partner with quants to optimize research models for production speed and reliability.
• Monitor model performance and continuously refine for accuracy and stability.

Requirements:

• 3–6 years’ experience in ML, quant research, or software engineering.
• Solid grasp of statistics, optimization, and time-series modeling.

Nice to have:

• Experience in low-latency or systematic trading environments.
• Familiarity with backtesting or model integration into execution systems.