Top AI & Machine Learning Jobs

Own the applied science direction for GenSim, Datadog’s generative simulation environment for training and evaluating AI agents. Define methodology and quality metrics for post-training data, improve simulation realism and failure difficulty, and develop scalable production systems integrated into training loops. Lead LLM and agent evaluation approaches, determine how synthetic data should be applied, and collaborate across engineering and applied science teams.

Design, research, and benchmark machine learning algorithms for anomaly detection, error outlier analysis, and faulty deployment analysis. Build, deploy, scale, and monitor user-facing ML features, models, services, and infrastructure in production. Analyze high-volume streaming data, communicate complex algorithms to nontechnical audiences, participate in research journal discussions, maintain team-owned systems, and join the on-call rotation while partnering with engineering and product teams.

Leads and grows an engineering team building AI-powered developer tools. Owns technical direction, architecture, hiring, career development, roadmap shaping, delivery execution, service quality, and operational ownership. Partners with Product and Design to turn ambiguous early-stage product problems into shippable solutions while improving developer cycle time, correctness, reliability, and developer experience.

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 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 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.

Design, train, optimize, and deploy compact, high-throughput anomaly-detection models for streaming telemetry. Build training-data pipelines, integrate models into production with GPU/latency/cost constraints, create interpretability tools and agentic validation layers, and maintain/monitor systems including on-call rotation.

Lead a team of engineers and applied scientists to build cost-efficient specialized AI models and security capabilities, own end-to-end delivery from research to production, define evaluation pipelines and success criteria, collaborate with product and research, and mentor/hire to grow the team.

As an AI Research Engineer, you will work with research scientists to build infrastructure, implement models, and enhance AI capabilities in systems for production code repair and observability.

As a Senior Applied Scientist, you will design machine learning solutions, develop scalable features, and analyze data within the Datadog app, collaborating with engineering and product teams.