Top Artificial Intelligence Software Engineer Jobs

Designs, builds, and productionizes healthcare knowledge graph solutions using LPG and RDF technologies. Develops FHIR data transformation and ingestion pipelines, ontology mappings, entity linking, provenance tracking, graph queries, and automated validation. Translates semantic specifications into scalable implementations while collaborating with ontology, terminology, clinical, FHIR, and data engineering teams. Participates in technical design, architecture reviews, code reviews, and engineering best practices.

Own and grow either the data engineering or product/web application team. Set technical direction, maintain engineering quality, guide architecture and delivery, mentor and evaluate engineers, hire talent, manage performance, and align engineering outcomes with customer and business goals. The role requires hands-on technical credibility, reliable execution, strong communication, and comfort operating autonomously in an early-stage defense technology company.

Build and scale backend systems, APIs, infrastructure, and applications for multimodal sensing and AI intelligence platforms. Responsibilities include processing sensor, audio, video, and environmental data; supporting data pipelines and model inference; ensuring reliability, scalability, and data integrity; and contributing to engineering standards and long-term technical strategy.

Develop and debug production-grade full-stack applications across front-end and back-end systems. Build features, APIs, databases, and data models; troubleshoot end-to-end issues; analyze unfamiliar codebases; translate implementations across programming languages; and review AI-generated and developer-written code for correctness, security, scalability, performance, and maintainability.

Build and customize enterprise full-stack applications, data connectors, and plugins for customer technology environments. Develop dynamic front ends using React, AngularJS, or NextJS; support backend development and database management; and implement UI event instrumentation. Collaborate using Agile/Scrum, CI/CD, and cloud-native practices while helping lead engineering teams. Bonus work includes generative AI, prompt engineering, search and AI APIs, cloud platforms, and legal technology.

Lead full-stack software development across frontend and backend systems, designing scalable solutions and productionizing AI-driven capabilities. Collaborate with product, design, and support teams to translate customer needs into technical solutions, resolve complex issues, improve development processes, and contribute to product direction. The role requires React, TypeScript, and Node.js expertise, with preferred experience in LLMs, REST APIs, microservices, databases, and cloud platforms.

Build and maintain secure, scalable APIs and infrastructure for an AI-powered fintech platform. Develop features across the stack, integrate third-party APIs, deploy infrastructure with Terraform, apply compliance and security practices, and support customers as a technical point of contact. The role involves close collaboration with the CTO in a greenfield, early-stage startup environment.

Build production software for supplier intelligence and supply-chain provenance. Develop document and data pipelines, LLM-powered agents, regulatory logic, ERP integrations, evaluation tooling, and customer deployments. Work directly with customers on plant floors and supplier calls while shaping architecture, engineering culture, and future hiring. The role requires strong full-stack development, production LLM experience, attention to audit-level correctness, and comfort working independently in an early-stage startup.

Own AI model-serving infrastructure for a platform with 10 million users. Optimize model fit within fixed GPU budgets, improve serving speed and latency, manage cost per token, determine when to optimize or rebuild systems, and plan capacity expansion. The role requires high ownership, comfort with ambiguity, and in-person work at the Palo Alto office.

Design, optimize, and scale distributed backend systems supporting foundational visual reasoning models. Responsibilities include building APIs, data pipelines, streaming frameworks, task schedulers, and state management systems; solving concurrency, reliability, and latency challenges; and leading fault-tolerant, highly available, auto-scaling infrastructure across distributed clusters. The role partners with research and engineering teams to deliver high-performance systems for multimodal AI workloads.