Top AI & Machine Learning Jobs

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

The Applied AI Developer will design and develop AI applications, build infrastructure for agentic AI systems, integrate APIs, and collaborate with stakeholders on AI solutions.

Own QA strategy and automation for agentic LLM and RAG workstreams: design AI-specific evaluation frameworks, build Python test suites (unit, integration, system), validate integrations (Salesforce, Slack, third-party APIs), integrate tests into CI/CD, run performance/load and security/adversarial tests, and ensure observability and regression detection for non-deterministic outputs.

The Manager of Data Science will lead a team focused on AI innovations utilizing LLMs, provide mentorship, and align technical strategies with business goals.

The role involves researching, designing, and prototyping generative AI solutions for cybersecurity. Responsibilities include building datasets, training models, and presenting findings to various audiences.

Lead and conduct mechanistic interpretability and alignment research for security-specialized AI. Develop methods to read model internals, detect misuse signals, design training interventions and evaluation frameworks, publish original research, and recruit and mentor a lean research team.

Lead the Data and ML Platform team, defining vision and strategy, overseeing platform development, fostering innovation, and managing a distributed team.

Engineer will design, build, and maintain scalable ML pipelines for data preparation and model training, collaborating with multiple teams.

The role involves maintaining and optimizing ML infrastructure, diagnosing complex issues, performing system optimization, and collaborating with engineers to resolve workflow issues.

The role involves designing and facilitating a Data+ML platform, building scalable ML pipelines, and collaborating with engineers to enhance data and ML capabilities.