Top AI & Machine Learning Jobs

Designs, develops, validates, and deploys computer vision and AI solutions for automated inspection, defect detection, process monitoring, and predictive maintenance in manufacturing plants. Integrates cameras, sensors, lighting, edge devices, and manufacturing equipment; leads proof-of-concept development, global deployments, vendor partnerships, documentation, and standardization. Engages plant and business stakeholders across regions to define priorities, develop roadmaps, and scale solutions.

Own the strategy, architecture, evaluation, and production deployment of Jerry.ai’s next-generation AI agents and workflow automation platform. Lead conversational agents, voicebots, computer-use agents, document validation, transaction orchestration, and external insurance-system integrations. Evaluate emerging AI technologies, make technical investment decisions, partner with engineering and OpenAI, and ensure systems meet requirements for reliability, latency, cost, regression prevention, and failure handling.

Develops fraud prevention rules and strategies using advanced analytics, statistics, structured and unstructured data. Manages fraud strategy projects, governance, data structures, stakeholder reporting, vendor relationships, and risk mitigation processes. Translates business requirements into analytical solutions, validates scalable analyses, monitors industry trends, and recommends strategies to business partners.

Owns Navitus’s enterprise AI governance and enablement program, including governance frameworks, risk assessments, compliance monitoring, use-case inventory, vendor AI evaluations, reporting, and committee operations. Partners with Legal, Compliance, Privacy, Security, IT, Engineering, and Learning Services to support responsible AI adoption. Leads proof-of-concept initiatives, develops policies and training strategies, tracks regulatory developments, mentors others, and presents governance insights and recommendations to leadership.

Design, build, deploy, and operate agentic AI systems, LLM tools, RAG pipelines, vector search capabilities, MCP connectors, and production data pipelines. Apply software engineering practices including testing, CI/CD, observability, and monitoring. Collaborate with scientific and business stakeholders to translate ambiguous needs into secure, governed technical solutions. Provide technical leadership in AI architecture, establish best practices, and mentor junior team members.

Supports NSWC Crane’s Model-Based Engineering Assessment Division through data science, modeling and simulation, mission engineering, RDT&E, and fleet-support work. Performs analytics, data mining, statistical and graph algorithm development, software integration and testing, ETL, API, and database-related tasks under government technical direction. Maintains quality, configuration-management, and security requirements. The role requires a master’s degree, 10 years of professional data science experience, and an active DoD Secret clearance or interim clearance.

Designs, configures, and deploys Agentforce agents and autonomous workflows within Salesforce Financial Services Cloud. Integrates agents with MuleSoft APIs, external data, and financial planning platforms; implements Einstein AI capabilities; and ensures security, compliance, and reliable agent behavior. The role also tests and monitors agents, mitigates hallucination risks, analyzes usage data, incorporates advisor feedback, and evaluates new Salesforce AI features.

Design, build, and deploy full-stack infrastructure and orchestration for autonomous AI growth agents. Own backend systems, event-driven workflows, data pipelines, front-end interfaces, and generative features from 0→1. Collaborate with founders to shape architecture and ship Hypesonic products that scale AI-driven marketing and invention workflows.

Build and operationalize production-grade image and video generative AI systems. Responsibilities include designing multimodal pipelines, agentic workflows, MCP servers, microservices, prompt management, model integrations, ML infrastructure, and scalable inference services. The role emphasizes reliability, cost, latency, observability, and collaboration with data scientists, designers, product managers, and creative teams to deliver AI-powered visual experiences.

Design and operationalize large language model fine-tuning workflows using supervised learning, DPO, RLHF, LoRA, and QLoRA. Build distributed GPU training pipelines, curate datasets, optimize training performance, and create rigorous safety and capability evaluations. Manage reproducibility, model artifacts, and training reliability while collaborating with cross-functional teams. The role also includes documenting results, mentoring engineers, and translating LLM research advances into production-ready solutions.