Top Entry Level Artificial Intelligence Jobs

Build the end-to-end architecture of an AI-powered software factory, including high-throughput telemetry capture, privacy pipelines, knowledge graphs, autonomous coding agents, and production deployment. Personally execute client workflow mapping and software delivery, then automate those processes for future deployments. The role requires strong product judgment, full-stack ownership, enterprise workflow optimization, and in-person collaboration in San Francisco.

Develop and optimize machine learning methods for intrinsically disordered proteins, including generative models for protein conformational ensembles, affinity prediction, and generative chemistry. Build scalable training pipelines, adapt research methods, and collaborate with computational and experimental scientists to validate models using biophysical data.

Build and improve a full-stack product that enables customers to manage AI agent workflows, including exception queues, reconciliation reviews, approvals, reporting, APIs, and agent SDK tools. The role involves frontend and backend development, real-time interfaces, workflow reliability, traceability, customer feedback, and polished user experiences. Engineers work in person with customers and a small founding team to shape product direction and solve ambiguous technical problems.

Build and optimize HubSpot environments across multiple clients, including CRM architecture, data governance, workflows, automations, integrations, APIs, webhooks, migrations, reporting data, and troubleshooting. Audit data quality, resolve integrity issues, establish scalable standards, document systems, and improve CRM reliability and performance within an agency or client-services environment.

Design and operate Trajectory’s recruiting system, including talent identification, engagement, outreach, interview pipelines, candidate closing, recruiting events, funnel metrics, and AI-powered automation. The role partners closely with founders to attract exceptional technical candidates and creates scalable processes for sourcing, assessing, and hiring talent.

Build and optimize infrastructure, developer tooling, build systems, CI/CD, APIs, services, and deployment platforms. Investigate performance bottlenecks across software, operating systems, storage, networks, and hardware; improve observability, reliability, provisioning, and recovery. Develop internal platforms used by multiple engineering teams, maintain homelab infrastructure, and measure the impact of performance and workflow improvements.

Lead and grow the platform engineering team while providing hands-on technical direction. Design and build scalable enterprise platform systems covering identity, access management, integrations, extensibility, reliability, security, compliance readiness, and operational excellence. Translate customer needs into technical deliverables, mentor engineers, and guide development in an ambiguous startup environment. AI agent or production LLM experience is beneficial.

Generates and qualifies new business opportunities through strategic outbound sales. Maintains lead records and prioritization in Salesforce, collaborates cross-functionally on top-of-funnel strategy, and provides market feedback to product and engineering teams. The role requires strong communication, organization, analytical ability, adaptability, and a self-starting mindset in a fast-paced startup environment.

Build and manage outbound sales campaigns targeting major insurers and Fortune 500 companies. Research prospects, identify stakeholders, conduct multichannel outreach, qualify opportunities, book qualified meetings for Account Executives, and improve the outbound playbook through prospect feedback. This entry-level, in-office role requires strong communication, resilience, organization, coachability, and ambition to develop a sales career.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.