Top AI & Machine Learning Jobs

The ML Engineer will develop and deploy machine learning models, collaborate with teams, analyze datasets, and improve model performance.

Build and deploy AI-enabled workflow automations and agentic systems across the Office of the CEO, Capital Markets, Finance, and Operations. Integrate LLMs and foundation models with BI/data infrastructure, prototype and scale production-ready copilots and RAG solutions, and document/enable reusable templates while ensuring data security, governance, and stakeholder adoption.

Design, develop, and deploy Agentforce AI agents and custom actions on Salesforce. Build Apex, LWCs, flows, and secure API integrations with legacy federal systems and Data Cloud. Ensure AI safety, compliance, testing, and support CI/CD/DevSecOps practices in cleared federal environments.

Design, implement, and maintain AI-driven tools and workflows (initially Microsoft 365 Copilot, Copilot Studio, Power Platform). Translate business needs into secure, governed AI solutions, manage data sources and access controls, build automations, document designs, implement guardrails, and train users to drive adoption.

Lead and scale a global engineering organization to deliver enterprise-grade AI/GenAI SaaS products. Define technology strategy and roadmap, drive architecture for LLMs/RAG/NLP systems, ensure scalability/reliability, establish DevOps and quality practices, partner with Product/Sales/Customer teams, and manage hiring, budgets, and leadership development.

Lead creative direction, performance-driven testing strategy, and client relationships for key accounts. Translate client needs into strategic briefs, pitch concepts, steer creative toward measurable performance, and manage internal teams to deliver winning creative rapidly. Be hands-on in AI production, refine concepts for testability, and drive retention through persuasive communication and results.

Engram builds models that create compact, personal "engrams" (memories) to make AI context-aware and efficient. This posting invites interested candidates to submit an expression of interest for future roles or opportunities.

Lead enterprise AI strategy, governance, and adoption. Establish AI policies and responsible-AI practices, prioritize business use cases, align AI investments with organizational goals, and drive measurable business value from AI solutions across the company.

Lead development and execution of AI-specific test plans to evaluate LLM and AI system quality, robustness, security, and compliance with OWASP AI Testing Guide and related standards. Document defects and remediation, capture evidence, validate model behavior (prompt-response, hallucinations), implement AI-driven QE approaches (autonomous test generation, self-healing automation, defect prediction), and report results to stakeholders.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.