Machine Learning Infrastructure Engineer, GenAI Technology
Point72 Asset Management - United States
Posted Apr 20, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Machine Learning Infrastructure Engineer, GenAI Technology United States A Career with Point72's Technology Team As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm's IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We're a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications. As a member of Point72's Technology team, we encourage and support your professional development from day one-helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity-all while delivering real business impact for our multi-billion-dollar global business. WHAT YOU'LL DO - Design and implement high-performance infrastructure to support large-scale generative AI and machine learning workloads, enabling faster model iteration and real business impact - Design and operate distributed systems for model training, hyperparameter tuning, inference, and data preprocessing pipelines to deliver reliable end-to-end machine learning (ML) workflows - Collaborate with ML researchers and engineers to produce models, optimizing compute utilization, training throughput, and inference latency - Develop and automate deployment, orchestration, and CI/CD pipelines for models and data workflows using container orchestration and infrastructure-as-code (IaC) - Implement observability, monitoring, and cost-management strategies for GPU and accelerator compute environments to maintain
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