Point72
Machine Learning Engineer, GenAI Technology
About this role
Point72 seeks a Machine Learning Engineer to design and deploy scalable AI/ML systems that power investment decision-making across the firm. You'll collaborate with data scientists, engineers, and compliance teams to integrate cutting-edge AI solutions into production platforms while maintaining enterprise standards.
What you'll do
- Develop and maintain scalable AI/ML architectures and systems for production environments
- Collaborate with data scientists, engineers, product teams, and compliance to integrate AI/ML solutions
- Evaluate and select tools, technologies, and processes to optimize AI/ML system quality and performance
- Monitor industry advancements in AI/ML technologies and methodologies
- Ensure compliance with industry standards and best practices in AI/ML development
- Communicate complex technical concepts to both technical and non-technical stakeholders
What they're looking for
- Machine learning frameworks (TensorFlow, PyTorch, Scikit-learn)
- Python programming
- Java or C++ programming
- AI/ML system architecture and design
- Problem-solving and independent thinking
- Cross-functional collaboration
- Technical communication and stakeholder engagement
- Compliance and ethical standards awareness
Benefits
- Fully-paid health care benefits
- Generous parental and family leave policies
- 401(k) savings program with employer match
- Mental and physical wellness programs
- Tuition assistance
- Volunteer opportunities and employee affinity groups
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Point72
Point72 operates trading, data infrastructure, and investment management technology platforms that power systematic portfolio management and capital markets operations. The company is hiring infrastructure engineers, data reliability specialists, and software engineers to build and maintain mission-critical systems including data pipelines, network architectures, storage platforms, and research tools.
- Website
- point72.com
Likely interview questions
- Describe a complex ML system you built from scratch—what were the key architectural decisions and trade-offs you made?
- How do you stay current with rapid advances in AI/ML, and can you give an example of a new technology you've recently adopted?