Top Artificial Intelligence Software Engineer Jobs

Own the workflow engine powering customer runs, including scheduling, queues, retries, batching, idempotency, provider integrations, observability, and cost controls. Scale Postgres and Redis while improving reliability across high-volume data workflows. Work autonomously, use coding agents extensively, and make technical decisions for an early-stage platform.

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.

Design and scale highly available backend services for an AI-powered manufacturing platform. Build AWS-based event-driven systems with Node.js, REST/GraphQL APIs, and database integrations. Use AI coding agents while validating correctness, write tests and documentation, improve observability and reliability, debug production issues, and collaborate with product, frontend, and infrastructure teams. Contribute to technical design, code reviews, engineering best practices, and responsible adoption of AI-assisted development.

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.

Develop Linux-based services, provisioning automation, deployment tooling, industrial protocol integrations, and reliability features for edge computers and factory servers. Support containerized and virtual-machine applications, ML inference workloads, monitoring, logging, recovery, testing, debugging, and technical documentation. Collaborate with senior engineers while owning well-defined platform components.

Build and deploy production-ready AI agent solutions for enterprise customers. Responsibilities include designing integrations with customer systems, developing full-stack tooling in Python and TypeScript, operating cloud-native infrastructure, supporting technical sales engagements, translating customer feedback into product improvements, and leading implementations from discovery through deployment. The role requires strong software engineering, technical pre-sales, API integration, communication, and problem-solving skills.

Build and operate hospital integrations, including APIs, authentication, data mapping, monitoring, and production data pipelines. Work directly with hospital IT and clinical teams to investigate problems, develop customer tools and analytics, and turn recurring needs into reusable connectors and product features. The role requires strong backend, API, data modeling, and SQL skills, plus ownership of complex third-party integrations and customer workflows.

Develop scalable backend services and APIs, create coding tasks and deterministic verifiers for AI systems, debug complex codebases, optimize performance, refactor legacy systems, implement features, and provide technical reviews and reasoning. The role is remote, contract-based, and part-time at 15–20 hours per week.

Develop, test, deploy, and maintain Java applications supporting the DEA’s secure investigative case-management ecosystem. Collaborate with Pega developers, QA engineers, DevOps teams, and stakeholders in Agile delivery. Support CI/CD pipelines, application integrations, monitoring, troubleshooting, code reviews, testing, documentation, and secure software practices aligned with federal cybersecurity and compliance requirements.