Top Entry Level Artificial Intelligence Jobs

Own consulting workstreams from problem definition through research, analysis, and client-ready recommendations. Lead smaller engagements or modules, manage timelines and research programs, maintain data quality, create polished presentations and reports, and support proposals and reusable practice assets. The role requires rigorous analytical thinking, strong communication, attention to detail, and the ability to collaborate with senior stakeholders in a fast-paced consulting environment.

Own growth for Chatbase by identifying opportunities, launching and measuring experiments, optimizing acquisition channels, and scaling successful programs. Responsibilities include improving search and AI visibility, testing paid channels, expanding affiliate partnerships, tracking conversion and acquisition costs, and driving customer and revenue growth through hands-on cross-functional execution.

Source and recruit exceptional go-to-market talent, initially focused on post-sales accounting and finance roles. Research talent markets, map companies and teams, personalize candidate outreach, conduct initial screens, manage a small candidate funnel, and support candidates through interviews. Partner with recruiters and hiring managers to improve candidate evaluation, experiment with sourcing strategies, and use data and feedback to improve recruiting results.

Define the microarchitecture for photonic communication and interconnect fabrics. Propose photonic device designs, estimate performance, power, and area, collaborate with photonic and analog engineering teams, and author, review, and validate architectural specifications for next-generation AI and HPC products.

Provides administrative, customer, site, and event support at an in-person AI safety research center. Responsibilities include welcoming visitors, managing support requests, maintaining records, reserving rooms, preparing workspaces, troubleshooting office issues, ordering supplies, and assisting with events. The role is designed to develop broad operations skills and requires strong organization, communication, flexibility, attention to detail, and comfort working onsite with occasional evening and weekend responsibilities.

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.

Train and improve frontier AI models for scientific reasoning by curating scientific and synthetic data, building evaluations, applying distillation methods, and running large-scale experiments across thousands of GPUs. The role collaborates with researchers in reinforcement learning, physics, chemistry, and supercomputing to improve model capabilities and establish foundations for future pretraining.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Own high-volume events in San Francisco from concept through follow-up, then transform each event into social content that expands reach and drives awareness and pipeline. Build and activate a startup ecosystem network, manage logistics and guest lists, create recaps and multimedia content, measure performance, and develop the events and community playbook independently within an early-stage distributed team.

Lead the development of Ello’s growth-engineering function and ship end-to-end features across mobile, web, backend, and lifecycle channels. Identify growth opportunities, run experiments, define metrics, improve acquisition and retention journeys, and build analytics and experimentation systems. Collaborate across product, design, marketing, and engineering while using quantitative and qualitative insights to drive measurable user and business outcomes.