Top Artificial Intelligence Software Engineer Jobs

Own frontend engineering and product design from concept through production. Build AI-driven interfaces that make recommendations transparent, explainable, and controllable; create the design system and reusable component library; and translate complex operational workflows and real-time data into dependable experiences. Work directly with operators and field teams, including regular site visits, while establishing frontend standards for accessibility, responsiveness, performance, and quality.

Develop and maintain high-quality .NET desktop applications using C# and WPF. Collaborate with product owners and designers to deliver UI features, build maintainable and testable frameworks, refactor code, improve performance and stability, and develop proof-of-concept components. Apply MVVM, asynchronous programming, multithreading, SOLID principles, Agile practices, and full SDLC knowledge.

Develop C#/.NET desktop manufacturing software for optical transceiver testing. Build WPF/MVVM interfaces, concurrent test execution, hardware drivers, instrument resource management, RPC services, and embedded Python testing capabilities. Design reliable, testable systems using clean architecture, MongoDB, GitHub Actions, and automated deployment to Windows machines. Diagnose distributed failures through logs and telemetry while supporting software operating at manufacturing scale.

Leads a front-line software engineering team responsible for clinical report-generation, rendering, and rules platforms. Owns hiring, coaching, delivery, technical quality, regulated change control, automation, testing, observability, production readiness, incident response, and compliance with SOX and FDA requirements. Partners with product, quality, regulatory, and site-reliability teams while remaining engaged in architecture and code reviews.

Architect and lead development of Kong Identity’s multi-tenant platform, including token management, claims engines, federation, client management, security controls, compliance, observability, and developer tooling. The role focuses on globally distributed, high-performance identity infrastructure, advanced OAuth and OpenID Connect capabilities, fraud prevention, extensibility, and enterprise integrations. The engineer will drive technical strategy, mentor teams, and support zero-trust, workload identity, and service mesh patterns.

Leads the engineering team responsible for LIMS user interfaces, applications, APIs, and data architecture supporting scientific workflows. Sets technical direction, shapes the roadmap, hires and develops engineers, and partners with scientific, product, ML, data, automation, and platform teams. Ensures systems provide reliable, auditable, traceable, and reproducible records of experiments, samples, protocols, results, and laboratory operations while integrating with automation and platform infrastructure.

The GPU Kernel Engineer will design and optimize GPU kernels for large-scale AI systems, integrating them into ML frameworks and collaborating across teams to enhance performance.

Own Greenboard’s go-to-market technology stack, including HubSpot administration, CRM workflows, integrations, data quality, call intelligence, LLM-powered automation, dashboards, and Slack reporting. Partner with revenue leadership to translate sales processes into scalable systems, investigate funnel and deal data, and ship automation and analytics projects end to end. The role requires strong CRM expertise, SQL, Python or JavaScript, data warehousing, APIs, and experience building executive-facing reporting.

Build and support reliable dashboards for AI infrastructure and financial models, including TCO, tokenomics, and compute capital models. Responsibilities include implementing features, loading model releases, validating data, developing accuracy checks, collaborating with analysts, creating charts and explanations, answering client questions, and contributing to research newsletters. The role requires strong Python or TypeScript skills, software engineering experience, numerical accuracy, and effective use of AI coding agents.

Build and operate production custody systems: signing service, key management, chain integrations, and transaction pipelines. Own end-to-end subsystems, integrate new chains, collaborate with cryptography on threshold signing and key recovery, write post-mortems, and produce clear async design docs and PRs.