Top Entry Level Artificial Intelligence Software Engineer Jobs

Develop and maintain full-stack features for a SaaS platform supporting clinical trial workflows. Responsibilities include Python-based application development, frontend collaboration, architecture, testing, code review, refactoring, reliability, data integrity, and regulatory compliance. The engineer will collaborate with product, design, machine learning, infrastructure, clinical, QA, and regulatory teams while contributing to a distributed engineering organization.

Build and extend Mason, an AI-native property management assistant, across the full stack with a backend bias. Responsibilities include orchestrating multi-step LLM workflows, improving agent reliability, integrating with customer systems, extracting context from conversations and invoices, managing pipelines, and coordinating complex workflows among owners, tenants, vendors, and field agents. The role requires autonomy, attention to detail, customer engagement, and frequent in-person collaboration in San Francisco.

Build and deploy autonomous AI agents for enterprise customers. Own implementations end to end, including understanding customer workflows, developing integrations, shipping production agents and evaluations, and engineering systems for continuous improvement. Translate customer feedback and recurring deployment challenges into platform capabilities and product direction. Collaborate directly with customers and founders in a fast-paced, in-person startup environment.

Builds simulation infrastructure and software-in-the-loop sensor simulation products that help customers test and validate autonomous driving, drone delivery, eVTOL, robotics, and other physical AI systems safely at scale.

Build internal fullstack products and human-robot interfaces for operating, monitoring, debugging, and deploying robots. Responsibilities include realtime video and robot-state systems, safety-focused UX, on-device and cloud architecture, fleet versioning, observability, workflow measurement, and direct user-driven product development. The role partners with researchers, operators, and roboticists across lab, warehouse, and field environments.

Develop and maintain COBOL mainframe applications, translating requirements into programs, testing and debugging changes, supporting production, correcting data errors, and creating reports and documentation. The role uses COBOL, JCL, DB2, Control-M, Java, AWS, PostgreSQL, and Kofax, while supporting software enhancements, code migration, deployment procedures, and conversion of COBOL applications to web-based systems.

Own end-to-end customer deployments of an AI platform by discovering business workflows, translating needs into technical plans, building integrations and prototypes, configuring agents and prompts, troubleshooting production issues, driving adoption, and feeding customer insights into product development. The role combines hands-on engineering, product judgment, customer engagement, and deployment ownership in ambiguous, early-stage environments.

Develop software using C# and Oracle or SQL databases, including PL/SQL or T-SQL. Preferred experience includes web services, WCF, Web API, and Silverlight. The interview process includes an initial phone interview followed by an in-person interview in Miamisburg, Ohio, or Jacksonville, Florida, depending on the candidate’s location.

Build and deploy AI-powered marketing solutions for enterprise customers. Responsibilities include scoping technical requirements, developing proof-of-concepts, integrating customer marketing infrastructure, building data pipelines and custom functionality, deploying AI workflows, supporting customer success, and translating deployment insights into reusable product capabilities. The role requires close collaboration with sales, deployment strategists, engineering, product, and growth teams in an in-person San Francisco environment.

Build and ship customer-driven fixes and features in SageOx’s core product. Engage customers through Slack and onsite onboarding, debug integrations, design workflows, educate users, create documentation, and synthesize customer feedback into product insights. The role requires independent software development, strong communication, comfort with AI tools, and occasional travel to customer sites while working onsite in Seattle.