Top AI & Machine Learning Jobs

Own Firecrawl’s internal AI stack, including agent harnesses, shared skills, configurations, model routing, MCP servers, hooks, evals, and tooling. Drive AI-agent adoption across teams, beginning with revenue, by building workflows, coaching employees, and overcoming resistance. Analyze token spend and output to improve cost per useful token through model selection, routing, caching, and workflow optimization. Build missing capabilities in code and stay current with rapidly changing AI development tools.

Leads Virginia Union University's enterprise AI strategy, governance, training, adoption, and responsible implementation. Develops standards, policies, risk controls, and institutional AI literacy programs while guiding the design, deployment, and monitoring of AI agents, automations, and retrieval-augmented systems. Partners with academic and administrative divisions to identify use cases, redesign processes, integrate approved platforms, measure impact, manage vendors, pursue partnerships and grants, and build an expanding AI team.

Leads Jabil’s enterprise AI strategy, architecture, governance, and adoption roadmap. Defines reference architectures and oversees initiatives involving generative AI, computer vision, digital twins, machine learning, intelligent automation, and MLOps. Advises executives, evaluates technologies, leads distributed technical teams, manages proofs of concept and deployments, establishes responsible AI practices, and drives innovation through patents, publications, training, and strategic investments.

Build secure, customer-facing cloud and web applications while modernizing legacy products and embedding valuable AI capabilities. Translate customer and business needs into scalable software solutions, lead projects from concept to delivery, and establish repeatable engineering practices. Responsibilities include architecture and implementation, testing, troubleshooting, stakeholder collaboration, mentoring junior developers, code reviews, and staying current with software development trends.

Supports the AI team in prototyping, testing, and delivering production-focused AI solutions for bank workflows. Builds AI agents and applications, integrates enterprise systems through APIs and MCP, enhances AI platforms and workflow automations, evaluates emerging models, and measures adoption and business impact. Collaborates with engineering and business teams while following governance, security, testing, and banking compliance requirements.

Build AI data and evaluation platforms for autonomous aircraft, transforming flight telemetry, sensor, imagery, and simulation data into curated datasets, metrics, and workflows. Develop scalable annotation, data quality, evaluation, visualization, and machine learning solutions for perception and decision-making. Collaborate with autonomy, controls, and flight software teams while supporting system reliability, anomaly investigation, safety validation, and production ML deployment.

Own Arcade’s end-to-end machine learning lifecycle, including training pipelines, model development, evaluation, production telemetry, and releases. Build compact, efficient models for tool selection, routing, retrieval, recommendations, and agent memory. Productionize models for customer VPCs and air-gapped environments through quantization, optimization, packaging, and serving. Set the ML strategy and stack, establish evaluation practices, convert agent traces into training data, and help grow the ML team.

Supports the design, development, and governance of AI and ML architectures for enterprise modernization. Responsibilities include building cloud data pipelines, AI governance frameworks, agentic AI and RAG architectures, vector database integrations, model monitoring, explainability, bias detection, drift management, and architecture documentation. Collaborates with enterprise architects and engineering teams while supporting governance reviews and technical assessments.

Build and deploy AI workflows using AIVA, designing prompts, tool integrations, evaluation frameworks, guardrails, exception handling, and human-in-the-loop patterns. Integrate AI solutions with APIs, databases, and enterprise systems, then rapidly test and refine solutions with clients. Collaborate with technical leads, business analysts, integration engineers, and operations subject matter experts to deliver production-quality AI applications.

Develop agentic AI systems and machine learning methods for drug discovery, including molecular and tabular in-context learning, few-shot adaptation, foundation models, and biological data analysis. Lead research projects from task definition and dataset construction through model training, benchmarking, ablations, and scientific validation. Collaborate with chemists and biologists, build reproducible research software, scale training pipelines, and translate successful methods into drug discovery capabilities and scientific publications.