Top Remote Entry Level Artificial Intelligence Jobs

Own data ingestion, transformation, exchange, and integration workflows between K–12 Student Information Systems and Timely’s scheduling platform. Ensure pipeline accuracy, security, reliability, and scalability while collaborating with engineering, product, customer success, district IT teams, and SIS vendors. Develop documentation, process improvements, automation scripts, and lightweight Python tools; lead discovery for new SIS integrations and help scale the data integrations function.

General application for future opportunities at Intetics across software development, quality assurance, business analysis, project management, design, and other fields. Applicants should submit an updated CV and may provide relevant skills, experience, preferred roles, location, working arrangements, availability, compensation expectations, and professional portfolio links. Recruitment will review profiles for suitable future openings.

Own customer outcomes for Gasworks, an agentic platform. Deploy and operate the product in customers’ repositories, CI systems, clouds, and identity environments; debug distributed systems; write customer-driven fixes and integrations; and turn deployments into repeatable onboarding playbooks. The role combines software engineering, customer-facing implementation, product feedback, technical documentation, and daily use and verification of coding agents. Some customer travel is required.

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.

Develop and evaluate new AI and HCI product concepts through hypotheses, prototypes, experiments, and user testing. Build model harnesses, improve memory and context retrieval, and collaborate with ML researchers to connect product and model feedback. Determine which ideas should advance into products, document unsuccessful experiments, and own implementation and direction for successful product areas.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Own and grow partnerships with agencies, technology companies, platforms, and product companies. Responsibilities include building partner pipelines, negotiating agreements, activating partners, managing co-marketing and co-selling initiatives, tracking partner-sourced revenue, and collaborating with sales and leadership to convert opportunities into product development projects.

Organize and curate large volumes of raw information: extract key points from transcripts, maintain clean spreadsheets, curate links/resources, tag and categorize content, and follow or propose systems for information structure. Part-time remote role with initial 10 hours/week and potential to grow.

Own developer relations for an AI safety platform by leading technical launches, creating content, organizing hackathons and meetups, growing open-source communities, and building partnerships with AI labs and developer tool companies. The role also contributes to promotional videos, tracks adoption metrics, and engages engineers and AI researchers across technical channels.

Own the government pursuit pipeline by identifying and qualifying SLED and federal opportunities well before RFP release. Build a rolling calendar, score opportunities, map stakeholders and incumbency advantages, recommend bid/no-bid decisions, and provide AEs with a ranked shortlist of winnable pursuits. Establish qualification criteria from win/loss analysis, maintain accurate pipeline data, support opportunity shaping, and protect win rates as bid volume grows.