Top Entry Level Artificial Intelligence Jobs

Prepare, review, and process invoices; verify billing information; maintain billing records; investigate and resolve discrepancies; answer basic billing questions; organize documentation; and collaborate with internal teams to resolve billing issues.

Own and grow OpenArt’s global creator community of 1,000+ creators. Build creator relationships, ambassador and referral programs, community events, and growth initiatives that drive advocacy, distribution, user acquisition, revenue, and product adoption. Partner with Product and Marketing on launches, early access, testing, creator feedback, and content generation while developing the Bay Area creator network.

Develop high-performance L4 autonomous driving planning algorithms and systems. Responsibilities include optimizing end-to-end model outputs with safety fallbacks, designing redundant low-compute systems, processing road-test data, and improving simulation platforms using real-world driving data. The role requires software engineering, algorithm design, problem modeling, and collaboration skills, with preferred experience in motion planning or robotics.

Build and productionize Agnes, an AI supply chain manager powered by LLMs and agents. Responsibilities include selecting and integrating LLM APIs, developing robust prompts, tools, structured outputs, and multi-step workflows; designing evaluation pipelines; monitoring LLM quality and performance; and handling messy real-world data. The role owns AI systems end-to-end, from prototypes through production, with bonus opportunities to deploy self-hosted LLMs.

Lead a software engineering team supporting Unity on an Android-based device ecosystem. Own execution and technical strategy, optimize Unity runtime performance through benchmarking and analysis, collaborate with engine, platform, graphics, product, and external teams, and communicate progress and vision. Manage, mentor, and develop engineering talent while enabling AI-assisted development workflows and platform-specific experiences.

Prospect and qualify leads, conduct multi-channel outreach, book meetings for the sales team, and manage activities within a modern CRM. The role supports the top of the sales funnel and involves close collaboration with leadership, Sales, Marketing, Product, and Operations. Locate.ai provides training, mentorship, and opportunities for career advancement.

Own technical customer inquiries and escalations from intake through resolution. Investigate complex issues, troubleshoot APIs and integrations, identify root causes, and coordinate Engineering and Product teams. Translate customer and business needs into technical action plans, communicate solutions clearly, prioritize high-impact issues, and identify recurring product gaps. Build scalable processes, documentation, and tooling while helping customers successfully use Higgsfield’s AI products.

Runs the research roadmap and data-generation programs from scientific signals through validated AI capabilities. Manages CRO and university research partners, including sourcing, contracting, quality oversight, timelines, and budgets. Coordinates science, AI/ML, and field teams; validates capabilities across locations and species; and builds research capacity through papers, advisory boards, grants, and partnerships. The role requires rigorous evidence standards, strong written communication, and comfort creating processes in an early-stage company.

Sources and engages approximately 100 qualified candidates weekly, primarily for engineering roles, using personalized outreach and sourcing tools. Maintains candidate pipelines and ATS data, supports interview coordination and process automation, and develops repeatable talent workflows. Partners with hiring leaders across Engineering, Product, Design, and GTM while representing the company in the talent community. This early-career role offers a path to recruiting within 12–18 months.

Build and own robot autonomy systems spanning task, behavior, path, and trajectory planning. Design long-horizon behavior architectures with recovery and operator handoff, coordinate multiple robots, integrate LLM/VLM reasoning safely, and define interfaces between learned policies and classical planners. Evaluate performance through simulation, log replay, regression testing, and real-robot field operation while collaborating across perception, controls, simulation, and platform teams.