Top Entry Level Artificial Intelligence Jobs

Build backend services and AI systems for legal drafting, review, and search workflows. Model legal knowledge into ontologies, develop retrieval and evaluation infrastructure, test LLM prompts, optimize queries and performance, and deploy solutions with customers. The role also includes occasional frontend work in TypeScript and React, customer collaboration, and end-to-end ownership of product features.

Open application inviting AI engineers, full-stack developers, data scientists, finance experts and others to apply. Candidates should submit a resume/LinkedIn, links to work (GitHub, Kaggle, portfolio) and a brief note explaining motivation, value they bring, and desired roles or projects.

Lead cross-functional AI data delivery projects from planning through implementation and adoption. Own project quality, timelines, economics, communications, risk resolution, and stakeholder transparency. Coordinate engineers, supply managers, clients, and other teams while managing project operations, status updates, retrospectives, and process improvements. The role involves client-facing delivery of data generation, evaluation, and red-teaming projects supporting large language model development and safety.

Leads federal opportunity capture strategy, pursuit coordination, competitive analysis, teaming, pricing, and win-probability improvement. Develops capture plans, evaluates customer needs and competitive landscapes, coordinates cross-functional teams, supports key-personnel recruiting, and presents progress to executives. Provides surge support for proposal management, content development, reviews, and sales-process improvement. Requires federal contracting knowledge, capture and proposal expertise, analytical ability, and familiarity with government contracting databases and tools.

Build interactive experiences across the company website, product, and special projects. Prototype ideas, refine interactions and motion, create visual essays and data visualizations, and develop responsive, accessible, performant websites. Collaborate with designers, marketers, and engineers from concept through production while creating reusable components and patterns. The role requires strong design instincts, frontend development ability, attention to typography and interaction details, and comfort with modern frontend and animation libraries.

The Content & Prompt Operations Specialist manages AI-assisted content workflows, ensuring accuracy and engagement in content produced. Responsibilities include writing prompts, drafting content, proofreading, and improving AI outputs while following structured processes.

Two-year leadership development program beginning in Level 1 customer support. Responsibilities include resolving client tickets, troubleshooting platform access, guiding dealership managers through data and reporting, and building customer-facing, product, sales, and operations expertise. After 12 months, participants advance to Associate Client Success Manager or Level 2 Implementation/Support Associate, followed by progression into Customer Success, Product Management, Marketing, or Sales.

Own and grow the U.S. corporate sales pipeline for AI-driven software development training. Responsibilities include engaging decision-makers, identifying business needs, tailoring training solutions, selling multi-day programs and ongoing enablement, developing long-term client relationships, and contributing to pricing, packaging, and go-to-market strategy. This is a remote, commission-based, consultative B2B sales role with potential for a future base salary.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build and improve ML and data pipelines, ship full-stack product features, and enhance Mac and Windows desktop applications. Work directly with enterprise customers to clarify requirements, deploy solutions, and turn customer learnings into repeatable product improvements. The role requires high ownership, rapid execution, strong engineering practices, and measurable customer outcomes across applied ML, AI systems, product, and infrastructure.