Top Artificial Intelligence Jobs

Own the AI roadmap for Operations, Finance, and HR systems, identifying and prioritizing automation opportunities across supply chain, inventory, accounting, and people processes. Translate business needs into product requirements, lead pilots and delivery, drive adoption, measure ROI, and communicate outcomes to executives. Partner with engineering, data science, IT, security, and vendors while ensuring AI governance, privacy, compliance, and operational reliability.

Lead AI research projects addressing complex energy-sector challenges through machine learning, deep learning, generative AI, and foundation models. Develop prototypes and scalable software solutions, collaborate with researchers and business units, create patents, publish findings, and support grant proposals. Research applications include power generation, renewable energy, electric grids, robotics, and manufacturing.

Lead research and advisory on AI integration, API management, and orchestration. Produce market guides and vendor evaluations, advise enterprise architects, deliver presentations and client engagements, monitor trends, and collaborate across research teams to provide actionable, implementation-focused guidance.

Serve as an individual contributor supporting AI applications: gather and document business requirements, perform data and process modeling, manage application data, monitor system performance, support testing, training, change requests, and produce reports. Collaborate with data scientists, engineers, and stakeholders to translate business needs into functional specifications and improve AI system productivity.

Lead product safety and compliance for medical devices, interpret and apply IEC 60601/ISO 80601 standards, plan and oversee safety testing and certifications, support audits and lab processes, and collaborate with cross-functional teams to ensure regulatory compliance for respiratory care products.

The Research Associate 1 supports laboratory experiments in myeloma research, conducting data analysis, reviewing literature, and assisting with manuscripts and database management.

Manage onboarding, training, support, health, retention, and expansion for ~30 mid-market Swine accounts. Deliver high-touch engagement (monthly meetings, on-site visits), demonstrate ROI via insights reports, support sales, and coordinate with Product and Service teams. Maintain CRM records and roll out product features.

Customer-facing clinical ultrasound expert who supports product demonstrations, customer training, and post-installation clinical application support. Partners with sales and healthcare teams to optimize ultrasound utilization, deliver education across specialties, troubleshoot, and improve customer satisfaction and adoption.

Lead full-cycle recruiting for senior Data, ML, and Engineering roles, partner with senior leadership on hiring strategy, own sourcing and candidate experience, negotiate offers, maintain Greenhouse data and reporting, and elevate recruiting best practices and DEI efforts.

Lead AI products end to end, from prototyping and evaluation through deployment, monitoring, and iteration. Own matching, search, ranking, embeddings, and LLM-powered features while shipping production code across Python services, Rails, and React. Develop evaluation systems for nondeterministic AI, analyze product data with SQL, run experiments, review code, and deliver measurable improvements to healthcare career marketplace products.

Develop and enhance data warehouses, BI platforms, Tableau dashboards, and self-service analytics solutions. Analyze data, fulfill rapid reporting requests, identify actionable insights, automate reporting processes, and collaborate with business, BI, and IT stakeholders to improve data quality and decision-making.

Designs and maintains AI-powered full-stack applications using React, TypeScript, Python, and FastAPI on Azure. Builds cloud-native, containerized solutions, data pipelines, CI/CD workflows, Terraform infrastructure, dashboards, and secure production systems. Integrates LLMs, AI agents, and machine learning models while partnering with data scientists and business stakeholders. Provides technical leadership, establishes engineering standards, mentors team members, and manages the full software development lifecycle.

Design and develop scalable AI platforms and developer tools using Python, MLOps, LLM prompting, and agentic workflows. Build ETL and data pipelines, integrate and deploy models on cloud infrastructure, implement monitoring and reliability dashboards, prototype generative AI applications, and establish testing, documentation, security, and ethical AI standards. Collaborate with data, engineering, and product teams to operationalize intelligent applications.

Leads enterprise semantic layer, data, and AI platform architecture. Defines lakehouse, medallion, data model, data contract, metadata, schema governance, feature store, model lifecycle, and interoperability standards. Partners with product, engineering, platform, and data science teams to create secure, reusable foundations for analytics, machine learning, and AI applications. Translates business and governance requirements into scalable technical architectures and provides thought leadership across enterprise data and AI enablement.

Lead product vision and roadmap for AI infrastructure and agentic systems. Work with engineering, data science, and sales to define requirements, prioritize features, enable execution, validate deliverables, maintain technical documentation, support go-to-market launches, and track customer feedback and market trends to drive platform strategy and measurable outcomes.

Lead technical direction for cloud platform engineering: design, build, and operate secure, scalable platforms supporting AI/ML and data initiatives. Drive DevEx, CI/CD, DevSecOps, observability, self-service developer workflows, and AI-assisted development including agent and MCP integrations.

Leads AI product strategy and MarTech enablement for marketing, from use-case prioritization and product development through production deployment, adoption, support, and ROI measurement. Oversees AI decisioning, orchestration, prompt engineering, responsible AI, and MarTech platforms while partnering with data, technology, privacy, legal, and agile delivery teams. Builds and mentors a multidisciplinary team, manages vendors, translates technical concepts for executives, and drives reliable PI planning and value realization across a matrixed organization.

Paid 10-week AI, data, and machine learning engineering internship. Interns will develop end-to-end data and AI/ML solutions, apply statistical and machine learning models, and contribute to projects involving incident resolution, computer vision, robotics, sustainability, and accessibility. Responsibilities include solution design, programming, debugging, testing, analysis, collaboration, and communicating technical concepts. The role is in person at Nike’s Beaverton, Oregon campus and includes relocation assistance.

Design and lead fairness and bias evaluations for AI-assisted People systems across hiring, performance, promotion, development, and workforce planning. Conduct algorithmic audits, subgroup and intersectional analyses, validation studies, counterfactual and generative AI testing, and human-AI workflow evaluations. Build scalable testing, monitoring, documentation, and reporting infrastructure. Partner with engineering, People, legal, privacy, and security stakeholders to recommend mitigations and communicate evidence, risks, limitations, and uncertainty to senior leaders.

Own concurrent healthcare customer implementations from contract handoff through stabilization. Manage stakeholders, phase gates, status reporting, discovery, workflow mapping, UAT, training, defect triage, and go-live reviews. Diagnose root causes using data and translate operational findings into AI agent configurations, routing rules, extraction fields, and escalation paths. The role combines project management, process design, customer partnership, and structured problem solving without production coding.

Supports analytics projects by understanding datasets, developing models, visualizations, reports, and actionable business insights. Collaborates with analytics teams, applies analytical tools to large datasets, adapts to changing business needs, and develops technical and leadership skills through a 12-week summer internship.

Lead end-to-end AI sector diligence: define key questions, design research, conduct expert interviews, synthesize findings to inform investment decisions, build methodology, and manage a 3–5+ junior-person pod at a founding-stage venture intelligence firm.

Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans, validate GPU and hardware-software interactions, and troubleshoot failures in large-scale distributed clusters. Collaborate with systems engineers using telemetry and profiling tools to identify root causes and performance bottlenecks. The role requires Python automation, test framework development, containerization, and CI/CD experience, with Kubernetes, infrastructure automation, networking, and firmware testing experience preferred.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

Leads development and deployment of computer vision and AI/ML systems for real-time object tracking, monitoring, and intelligent data triage. Designs scalable architectures, agentic data-collection workflows, and production MLOps pipelines; mentors engineers, oversees data lifecycle and model deployment, and translates client needs into AI/ML roadmaps. Requires advanced Python, computer vision, Docker, Kubernetes, seven-plus years of machine learning experience, U.S. citizenship, and an active Secret clearance or higher.