Top AI & Machine Learning Jobs

The Computer Vision AI & ML Engineer will develop and optimize deep learning models for robotics perception systems, collaborating across teams to integrate models and evaluate performance.

Design and operate AI platform infrastructure, ensuring security, compliance, and enabling self-service onboarding while collaborating across engineering teams.

The Research Engineer will develop scalable infrastructure for machine learning research, optimize workflows, and enhance model inference for trading systems.

The Data Scientist role involves developing measurement models, writing production-quality code in Python, and collaborating with teams to optimize data pipelines and ML workflows.

As a Staff AI Software Engineer, you'll design and build AI-native systems, lead model strategy decisions, and partner with product and engineering teams to deliver innovative AI solutions.

Lead an R&D ML team to develop and deploy AI/ML solutions for semiconductor manufacturing. Guide cross-functional research, mentor engineers in computer vision, physics-informed ML, and reinforcement learning, and apply materials science expertise to advance product development.

Conduct red teaming and adversarial evaluations of AI systems across violence, extremism, child safety, grooming, crisis, and other harm categories. Design evaluation frameworks, test scenarios, scoring criteria, taxonomies, and interventions that translate real-world harm expertise into structured technical outputs. Review model responses, exercise expert judgment, and produce methodology and findings for technical and government audiences while meeting ethical, legal, contractual, and data protection obligations.

Design and deploy agentic AI systems, build ML pipelines, optimize production models, and lead enterprise data standardization and governance initiatives. The role collaborates across business and technical teams to identify AI use cases, establish data quality and metadata standards, promote data literacy, advise on AI best practices, and ensure scalable, reliable, compliant AI solutions.

Lead end-to-end deployments of AI agents for procurement teams: map workflows, configure/test/tune agents, drive adoption and ROI, partner with Product/Engineering, and build repeatable implementation playbooks while identifying expansion opportunities.

Leads end-to-end delivery of production AI and machine learning solutions, owning architecture for LLM, RAG, embeddings, vector databases, and APIs. Mentors engineers, translates business needs into scalable technical solutions, oversees MLOps, CI/CD, monitoring, and reliability, conducts code reviews, and collaborates with stakeholders. The role remains hands-on through prototyping, troubleshooting, and development support.