Top Artificial Intelligence Software Engineer Jobs

Design and own backend infrastructure for a privacy-first AI memory platform. Responsibilities include multi-tier memory storage, secure multi-tenant isolation, real-time APIs, caching, LLM integrations, retrieval systems, synchronization, monitoring, deployment pipelines, and production reliability. The role requires close collaboration with ML engineering and product teams while owning architecture, infrastructure, testing, security, and operational troubleshooting.

Build and operate software platforms for infrastructure cost attribution, usage metering, budgeting, forecasting, capacity planning, and efficiency. Develop dependable data pipelines, shared models, APIs, reporting tools, and automated controls connecting infrastructure consumption to costs and ownership. Lead technical direction, collaborate across engineering and finance, improve resource utilization, ensure system correctness and scalability, and mentor engineers.

Build and deploy production-level solutions for Etched’s critical engineering challenges. Projects may include internal hardware tools, manufacturing execution systems, or agentic AI systems for R&D. The intern will own a project from scoping through production, working across full-stack engineering, infrastructure, and product development in a fast-paced, ambiguous environment.

Build AI-powered customer support workflows, tools, integrations, and knowledge systems. Investigate recurring issues, improve AI troubleshooting, automate repetitive support tasks, develop dashboards, and partner with Technical Support, Customer Success, and Product Engineering to improve customer experience and scalability.

Build and architect AI-powered software features that analyze contracts and fund documents, extract structured data, and automate legal workflows. Set technical direction, drive ambiguous initiatives from conception through production, improve engineering quality and tooling, and collaborate with product and design. Evaluate LLM systems, assess failure modes, and make strategic decisions about AI applications while mentoring and elevating other engineers.

Own the architecture and technical direction of Clarify’s data platform, including ingestion pipelines, eventing, integrations, canonical data models, public APIs, and webhooks. Build reliable systems that handle scale, retries, conflicts, backfills, and deduplication. Establish observability, operate production systems with on-call ownership, and mentor engineers through design reviews and pragmatic standards.

Designs, develops, configures, and supports ServiceNow solutions across ITSM, ITOM, HRSD, SecOps, and CMDB. Responsibilities include scripting, workflow automation, REST/SOAP integrations, system upgrades, troubleshooting, documentation, requirements gathering, stakeholder demonstrations, and Agile delivery. The role also maintains platform stability and applies ServiceNow best practices.

Lead customer-facing enterprise implementations of an AI platform. Build, deploy, and optimize full-stack integrations, reusable adapters, configuration layers, and generative AI solutions. Translate complex customer workflows into scalable systems, collaborate with technical and non-technical stakeholders, and improve product reliability, performance, and maintainability. Contribute field insights, documentation, and implementation playbooks while supporting discovery, prototyping, deployment, and post-launch optimization. Occasional travel under 10% is required.

Owns cross-subsystem software architecture for GE HealthCare’s Mac-Lab/CardioLab products while remaining hands-on in C++ and C# development. Leads technical design, implementation planning, performance optimization, system engineering, and developer guidance. Collaborates with clinical, cross-functional, and global teams to deliver quality medical software, apply AI-enabled development practices, and meet regulatory and product requirements.

Build and ship end-to-end features for an AI-first customer support product. Work with coding agents, design UI/UX and data models, own features from idea to production, and collaborate on tooling and feedback systems to make agent-driven development reliable and shippable.