Top Entry Level Artificial Intelligence Jobs

Review and annotate video assets according to project guidelines, maintain high accuracy and consistency, identify footage quality issues, and report edge cases or contradictions to the AI team. The role also contributes feedback on questionnaire design and process improvements during extended review sessions. This is a part-time, 10-week onsite contract in San Francisco, with possible extension.

Open expression-of-interest opportunity for professionals with demonstrated impact in areas such as engineering, policy, cybersecurity, auditing, standards, or program management. Applicants should describe an achievement that influenced corporate practices, public policy, widely used software, or their broader field, and explain their interest in working with AVERI. Formal credentials are less important than verifiable impact.

Own Phonely’s public voice and build its content engine from the ground up. Produce 5–10 or more pieces weekly across LinkedIn, YouTube, Meta, and other relevant channels. Manage the full production process, including story development, filming, directing, editing, publishing, audience engagement, and repurposing content into paid creative. Collaborate with engineers, sales representatives, founders, and customers to uncover compelling stories and represent the company at events, customer sites, podcasts, and launches.

Leads custom analog layout and verification for high-speed SerDes and memory I/O circuits in advanced semiconductor nodes. Collaborates with design teams on circuit implementation, supports IP and chip-level integration, assists with customer requirements and IP delivery, and applies high-speed layout techniques involving matching, parasitic minimization, power grids, ESD, EM, and IR-drop analysis.

Engineering talent community for current and future opportunities supporting tactical unmanned systems. Engineers may work in mechanical, electrical, embedded software, or software engineering, contributing to system design, development, integration, testing, troubleshooting, prototyping, and production. Roles involve cross-disciplinary collaboration, hardware interaction, and real-world product development for defense, national security, and public safety applications.

Architects and operates scalable customer-hosted cloud and on-premises deployments. Owns high-severity incident resolution, develops automated deployment pipelines and safeguards, builds observability systems, and creates internal tooling to improve developer velocity, reliability, and security. The role requires production Kubernetes and cloud infrastructure experience, strong Linux, networking, and infrastructure-as-code fundamentals, customer communication, and familiarity with AWS, Terraform, GitOps, IAM, and observability tools.

Train with engineering, product, and client teams before embedding in client engagements. Discover workflows through SME interviews, document review, and system mapping; identify automation opportunities; write specifications; build and test AI-agent prototypes; lead rollouts; and measure productivity and ROI. This early-career role offers development paths toward AI engineering, product and implementation, or engagement leadership.

Own Qevlar AI’s global demand generation strategy and pipeline number. Build an inbound-led, signal-based demand engine using SEO, CRO, AI discoverability, intent data, ABM, lifecycle marketing, and marketing automation. Manage website conversion, martech, agencies, events, and field marketing while partnering with Sales and Marketing. Report on pipeline, funnel conversion, CAC, and marketing-sourced revenue, using experimentation and AI automation to build scalable systems in an early-stage cybersecurity SaaS company.

Research and prototype next-generation Vision-Language-Action architectures for autonomous driving. Develop multimodal foundation models, world models, predictive simulations, long-horizon planning systems, and agentic driving architectures integrating reasoning, memory, planning, and action. Train and fine-tune large-scale models, implement current AI research, build data pipelines, and contribute research prototypes to production systems in collaboration with distributed global teams.

Develop generative and predictive machine learning models for antibody sequence, structure, and property design. Build multi-objective protein optimization and LLM-driven methods for lab-in-the-loop drug development, deploy models with engineering teams, and support therapeutic programs. Collaborate across machine learning, drug development, wet-lab science, automation, and data science teams to accelerate the creation of safer, more effective biologic therapies.