Top AI & Machine Learning Jobs

Leads enterprise AI strategy, roadmaps, and project portfolios from ideation through deployment. Manages AI scientists and engineers, partners with executives and stakeholders, translates business needs into technical requirements, and drives AI adoption, governance, education, and measurable business impact. Oversees solutions using analytics, machine learning, generative AI, data engineering, and related technologies while establishing success metrics and ensuring timely, budget-conscious delivery.

Conduct research and develop machine learning, generative AI, stochastic simulation, and optimization solutions for clinical trial operations. Build predictive models, optimization engines, LLM workflows, and data pipelines supporting enrollment forecasting, cost optimization, site selection, document processing, and risk management. Validate models, promote responsible AI and data quality, contribute reusable code and documentation, and communicate findings to technical and clinical stakeholders.

Build and launch AI agents for go-to-market teams across marketing, sales, product, and revenue operations. Responsibilities include designing prompts and agent personas, integrating LLMs with GTM systems, deploying and monitoring production agents, improving retrieval and model performance, documenting workflows, enabling users through training, and ensuring compliance with security, legal, and AI standards.

Lead and manage a team developing and operationalizing machine learning systems for structured and unstructured clinical data (EHR, text, imaging). Define technical strategy, oversee model lifecycle management, fine-tune and deploy foundation models, ensure data quality and model governance, partner cross-functionally, and mentor ML scientists and technical leaders.

Builds firmwide AI infrastructure and production-grade generative AI applications, including agentic workflows using RAG, semantic search, planning, and structured outputs. The role develops evaluation harnesses, guardrails, approval workflows, tool-use controls, source attribution, and kill switches while optimizing reasoning quality, retrieval accuracy, latency, cost, safety, and user experience. It also evaluates foundational and open-weight models for enterprise use.

Develops and supports secure AI-enabled applications and data integrations for a law enforcement agency. Responsibilities include training and optimizing machine learning models, integrating LLMs and agentic systems into on-premises applications, preprocessing structured and unstructured data, building RAG workflows, evaluating models, and ensuring CJIS and PII compliance. The role also involves collaboration, code reviews, testing, deployment, prototyping emerging AI tools, and maintaining data governance.

Leads enterprise AI strategy, roadmap development, and delivery of AI-powered solutions. Manages AI scientists and engineers, translates business needs into technical requirements, oversees AI project portfolios, and drives adoption, governance, education, and measurable business impact. Collaborates with executives, stakeholders, data scientists, and engineering teams to develop scalable analytics, machine learning, and generative AI solutions.

Develop and deploy production-grade 3D LiDAR and computer-vision perception systems for mobile robots. Own algorithms from sensor characterization through fleet deployment, while building AI agents and automated pipelines for data labeling, dataset curation, edge-case triage, and regression validation. Collaborate across controls and software teams, lead architecture and testing efforts, mentor engineers, and improve collision avoidance and autonomous behaviors across diverse operating environments.

Leads the architecture, development, and deployment of enterprise-scale agentic AI solutions. Translates business workflows into AI agents, orchestrations, skills, and plugins; establishes governance, safety, compliance, and observability practices; mentors engineering teams; evaluates emerging AI technologies; and shapes the AI platform roadmap. The role also drives organizational AI maturity through reusable architectures, training, experimentation, and production optimization across regulated industries.

Build and operate AI agents, GTM infrastructure, account-data systems, paid and ABM automation, CRM integrations, sending infrastructure, and deliverability programs. Own Salesforce, routing logic, integrations, data quality, observability, permissions, and auditability. The role is accountable for pipeline, revenue, reply rates, agent reliability, and cost efficiency. Candidates should have shipped production software, run scalable outbound or paid programs, applied AI to GTM workflows, and worked with Python or TypeScript, SQL, APIs, and Salesforce.