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

Lead GenAI/ML engineering projects within the APM team, focusing on model deployment, cross-team collaboration, and technical leadership.

Lead product strategy and corporate development for AI initiatives at Datadog. Define AI product direction, evaluate market opportunities, partnerships, and M&A, collaborate with cross-functional teams, and support go-to-market and roadmap decisions to accelerate AI capabilities across the platform.

Lead strategy and execution for Datadog's Agent Builder platform and ecosystem, enabling customers and partners to create, deploy, and operate AI agents. Define platform capabilities, extensibility (APIs/SDKs/integrations), developer workflows, governance, and evaluation. Partner with AI, platform, engineering, and go-to-market teams; engage customers and industry to evangelize operational AI and drive adoption.

Lead a team in developing AI and ML models focusing on detection and text intelligence. Collaborate with product managers and support team development for impactful AI features.

The Staff Applied Scientist will lead the evaluation strategy for AI agent integrations at Datadog, defining metrics and building datasets for quality improvements in agent performance through applied research and technical leadership.

The Distinguished Architect will provide technical leadership in AI, guide infrastructure discussions, and collaborate with stakeholders on AI observability solutions, requiring extensive experience and strategic thinking.

The AI Researcher will conduct advanced research in Generative AI and Machine Learning, develop specialized models for observability and code repair, and collaborate with product teams to integrate findings into Datadog's offerings.

The People Systems Developer designs and implements AI-native workflows for HR processes, focusing on talent acquisition and performance improvement through automation and integration of systems.

Lead evaluation strategy for Datadog Dashboards' AI-native system: define metrics, build eval datasets and regression harnesses, measure retrieval and tool-selection, reduce cost/latency, and provide technical leadership and cross-team mentorship to ensure production-quality agent behavior at scale.