Top Artificial Intelligence Software Engineer Jobs

Develop and maintain Python-based ground software supporting satellite mission operations. Responsibilities include software design, implementation, integration, testing, deployment, code reviews, troubleshooting, documentation, and verification activities. The developer will collaborate with systems engineers, program managers, government stakeholders, and cross-functional teams while supporting mission milestones and adhering to space-system software development and mission assurance standards.

Own customer data integrations for hospital systems and suppliers. Lead technical discovery, design and operate production data pipelines, troubleshoot live integration issues, build reusable connectors, and document support processes. Partner with product and engineering teams to improve the platform while communicating risks and timelines to technical and non-technical stakeholders. The role requires strong Python and SQL skills, production integration experience, customer-facing communication, and comfort working in ambiguous environments.

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.

Lead a team of software developers while remaining hands-on with C#/.NET development, architecture, coding, code reviews, and complex problem resolution. Guide technical decisions, mentor engineers, improve engineering standards and CI/CD practices, and deliver scalable, secure, reliable SaaS features. Collaborate with distributed Product and Engineering teams, contribute to system design, and help build a growing U.S. engineering organization.

Build and ship AI-native systems for enterprise customers across the full product lifecycle, from requirements gathering and research through experimentation, implementation, and iteration. The role includes customer engagement, product direction, and hands-on development across frontend, backend, and full-stack systems. Candidates should demonstrate strong engineering fundamentals, product judgment, experience building agentic systems, and an understanding of AI system behavior, failure modes, and improvement.

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.

Take AI-generated CEO code changes from prototype to production. Triage pull requests, reconstruct intent, rewrite or split code, add types, tests, error handling, safe migrations, and deployment safeguards. Protect accounting and ledger correctness, enforce LLM usage limits, maintain Sentry reliability conventions, and improve tooling to prevent recurring AI-generated mistakes. The role requires strong Python production experience, excellent code review judgment, candid communication, and the ability to work rapidly with the CEO and customer priorities.

Build, test, maintain, and deploy backend services for Eulerity’s marketing automation platform. Own features end-to-end, use AI agents and LLM tools for development, integrate third-party APIs, contribute product ideas, collaborate cross-functionally, and support documentation and knowledge sharing.

Design and implement secure, reliable synchronization and networking systems for distributed AI across edge devices, cloud environments, and air-gapped government systems. Architect frameworks using content-addressable storage, Merkle trees, and CRDTs; optimize distributed software under resource constraints; apply threat modeling and cryptography; collaborate on restricted-system deployments; and maintain testing, observability, and compliance readiness. The role requires 25–50% travel to offices, government field sites, and test locations.

Own and evolve patient-facing clinical application surfaces, including a React widget, clinician portal, and Python service layer. Lead full-stack architecture, AI application integrations, encounter data flows, internal tooling, versioned shared components, and operational reliability. Partner with AI engineers on LLM-enabled workflows, maintain dependencies, protect consumers from breaking changes, and mentor junior engineers. The role requires durable ownership of rapidly built systems and delivery of critical healthcare technology.