Top AI & Machine Learning Jobs in New York City, NY

Build and scale production-grade AI/ML systems for quantitative research. Responsibilities include translating research prototypes into maintainable software, optimizing GPU training and inference pipelines, designing data-loading and batching infrastructure, extending research libraries for agentic workflows, and improving codebase quality, testing, and scalability. The role partners closely with machine learning researchers and requires strong Python engineering, GPU systems experience, and familiarity with modern ML frameworks and agent technologies.

Lead research and production development for LLM post-training in quantitative finance. Responsibilities include designing RLHF, DPO, reward modeling, and policy optimization methods; building preference-data and training infrastructure; managing large-scale compute; developing evaluation frameworks; collaborating with quantitative researchers; and supporting production systems.

Conduct quantitative research for macro and fixed-income markets by developing alpha-generating investment models. Responsibilities include idea generation, feature development, alpha forecasting, monetization, portfolio construction, portfolio management, dataset analysis, and applying artificial intelligence and machine learning techniques. The role involves collaborating with quantitative researchers and developers to solve complex investment problems and communicate findings clearly.

Conduct AI and machine learning research during a 10-week summer internship. Develop techniques and infrastructure, write code, run experiments, analyze noisy financial data, and collaborate with experienced researchers. The role focuses on deep learning, large language models, reinforcement learning, data pipelines, cloud computing, and multi-machine research environments.

Conduct AI and machine learning research for financial-market applications, developing predictive models, infrastructure, prototypes, and production systems. Apply deep learning, large language models, and reinforcement learning to noisy data. Write code, run experiments on cloud and CPU/GPU infrastructure, collaborate across teams, contribute to products, and engage with internal and external research communities through publications and conferences.

Develop and deploy scalable machine learning, NLP, and LLM systems for unstructured financial data. Lead applied generative AI research, translate academic publications into production solutions, and collaborate with quantitative modeling teams on emerging NLP applications. Responsibilities include maintaining data-intensive production pipelines, prototyping research ideas, and delivering NLP and LLM services across research and production environments.

Research and develop hypotheses using diverse real-world datasets, apply advanced methodologies to extract meaningful signals, and rigorously test theories with engineers and business stakeholders. The role involves literature reviews, quantitative data analysis, clear communication of complex ideas, and ongoing engagement with the academic research community.

Develop sophisticated quantitative trading models using scientific methods, machine learning, statistical techniques, and large datasets. The researcher will create and test investment ideas, collaborate with engineers, follow current academic research, attend seminars and conferences, and share insights in statistics, machine learning, and data science.

Build and deploy production AI-powered systems across the firm. Responsibilities include designing agent-based workflows, integrating AI capabilities with APIs, MCP servers, data pipelines, and internal tools, and maintaining deployment, monitoring, observability, and operational infrastructure. The role requires autonomous problem scoping, architectural decision-making, collaboration with diverse teams, and practical experience building reliable software and agentic AI systems.