Top AI & Machine Learning Jobs

Own the end-to-end lifecycle of generative AI products across Success Academy’s school network. Translate stakeholder and business needs into AI/ML requirements, collaborate with engineering, data science, and UX teams, integrate LLMs and RAG pipelines, define evaluation metrics, run experiments, build user feedback loops, and optimize products for measurable efficiency and student outcomes. Contribute to enterprise AI strategy and manage products from discovery through launch.

Build, deploy, and maintain practical AI systems for SB Energy teams. Partner with domain experts to identify workflow bottlenecks, create Codex-powered agents and integrations, connect systems through APIs and MCP, and deploy reliable Azure solutions. Responsibilities include coding, testing, debugging, monitoring, security, user adoption, training, and measuring outcomes. The role requires hands-on software and AI integration expertise, strong Azure engineering skills, and the ability to translate ambiguous business needs into useful, maintainable solutions.

Leads the AI Governance Program for a regulated mortgage lending division. Establishes governance structures, policies, risk-tiering methods, controls, use-case lifecycle processes, third-party AI oversight, and audit-ready documentation. Coordinates reviews across legal, compliance, security, privacy, data governance, model risk, vendor management, and enterprise risk teams. Reports AI governance KPIs and KRIs to senior leadership while providing independent challenge and ensuring AI systems meet regulatory and organizational requirements.

Design and build production AI applications, agentic workflows, RAG pipelines, and multi-step automations using LLM APIs and orchestration frameworks. Integrate tools, retrieval systems, prompts, APIs, and infrastructure; create evaluation and observability systems; monitor reliability, latency, cost, and output quality. Translate ambiguous business needs into practical technical solutions, collaborate with stakeholders, and identify opportunities for AI-enabled process improvement.

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.

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.