Top AI & Machine Learning Jobs

Directs enterprise AI engineering strategy, architecture, implementation, operationalization, governance, and continuous improvement. Leads generative AI, RAG, agentic workflow, MLOps, and LLMOps initiatives; oversees integrations, cybersecurity and regulatory controls, vendors, costs, metrics, and solution delivery. Builds the AI engineering capability through team development, mentoring, innovation, and technical standards in a regulated life sciences environment.

Leads Verizon’s network automation and infrastructure AI organization, driving autonomous, self-healing operations across virtualized RAN and 5G Core. Oversees cloud-native orchestration, closed-loop assurance, AI/ML optimization, energy management, policy-as-code, and on-premises AI inference at national scale. Builds distributed engineering, SRE, MLOps, and network automation teams; establishes autonomy standards and roadmap toward TM Forum Level 4. Partners with network intelligence teams, standards bodies, hyperscalers, and executive stakeholders while ensuring production security, resilience, and compliance.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Leads end-to-end AI enablement consulting engagements, including readiness assessments, governance frameworks, champion programs, workshops, use-case discovery, adoption change management, and training. Develops executive-ready strategies, roadmaps, risk models, dashboards, and other client deliverables. Advises senior AI leaders, mentors junior staff, supports AI solution deployment, evaluates emerging tools, and contributes to business development and revenue growth.

Build and integrate machine learning systems using real-world sensor, document, and operational data. Responsibilities include developing Python data pipelines, training and evaluating models, integrating models or LLM workflows into tools, investigating failure modes, documenting results, and using version control, testing, code review, and experiment tracking. The intern will own a scoped project and present findings and recommendations at the end of the summer.

Designs and deploys production-ready AI solutions for strategic enterprise customers using Genesys Cloud, agentic AI, conversational AI, automation, data, and integrations. Leads discovery, architecture, prototyping, deployment, optimization, governance, and value realization. Advises executive stakeholders, connects technical decisions to business KPIs, develops reference architectures, guides responsible AI practices, and builds customer capability across complex enterprise environments.

Explore, prototype, and productionize AI solutions for clinical data review, focusing on LLMs, agentic AI, RAG, and intelligent workflows. Build modular agent systems, APIs, and interfaces; integrate clinical data sources; evaluate and monitor AI performance; and ensure scalable, reproducible, traceable, auditable solutions aligned with regulatory expectations. Collaborate with clinical experts, product managers, and engineering teams while contributing to AI innovation strategy.

Collect, clean, and analyze sports data; develop machine learning and predictive models; create visualizations and reports; collaborate with data and sports strategy teams; and monitor advances in sports analytics.

Leads the direction and execution of Modal’s LLM inference platform. Responsibilities include managing and growing an engineering team, making technical and product decisions, guiding distributed systems and inference-serving architecture, partnering with frontier customers, shaping optimization and product roadmaps, establishing reliability standards, and collaborating with compute strategy and go-to-market teams.

Define clinical reasoning, safety boundaries, evaluation scenarios, datasets, rubrics, and acceptance thresholds for AI-enabled chronic disease products. Analyze failures and edge cases, support clinical validation and outcomes research, interpret clinical and EHR data, and collaborate with Product, Engineering, AI/ML, Data, informatics teams, and clinical stakeholders on integrations and workflows.