Top Artificial Intelligence Software Engineer Jobs

Design, develop, test, debug, and maintain production-quality C and C++ software in Linux environments. Own features from requirements through release, applying object-oriented design, multithreading, performance optimization, memory management, and software testing. Use tools including GCC, Clang, GDB, CMake, Valgrind, Git, Jenkins, and Python. Collaborate with QA, product, and project stakeholders while contributing to documentation, code reviews, static analysis, and CI/CD automation.

Design, implement, test, and maintain resilient network software for teleoperated UGV swarms. Build low-latency video streaming and command-and-control systems, redundant connectivity and hot failover across mesh radios, satellite, and 5G networks, secure VPN solutions, and performance monitoring for throughput and latency. The role requires C++ or Python, Linux networking expertise, and collaboration in an onsite defense startup environment.

Design, build, and scale backend services, APIs, and workflow systems supporting healthcare claims identification and recovery. Integrate AI/ML models, legal datasets, and transactional data while developing fault-tolerant Temporal workflows. Maintain AWS infrastructure and secure deployment pipelines using Python, TypeScript, PostgreSQL, Redis, and related technologies. Collaborate with product, business, and data science teams to deliver reliable, secure, high-performance systems in a regulated startup environment.

Build and own the research platform for an AI-driven investment firm, including signal frameworks, backtesting engines, research APIs, financial data pipelines, evaluation tooling, and production trading systems. Ensure realistic simulations, data integrity, reproducibility, risk analysis, and protection against leakage and bugs. Collaborate closely with researchers and engineers, optimize numerical workloads, and manage deployment, monitoring, and reconciliation between live trading and backtests.

Own and evolve high-scale backend and data-layer services. Design, build, test, and operate distributed backend services and APIs, improve reliability/latency/scale, take end-to-end ownership from design through production support, participate in on-call and incident response, and mentor peers while partnering with Product and SRE teams.

Build software, interfaces, APIs, and data services that help scientists explore protein designs, plan experiments, and visualize screening results. Collaborate with scientists, engineers, and machine learning researchers, write tested and documented code, incorporate user feedback, and present the completed project. The internship focuses on delivering a practical tool supporting Profluent’s AI-driven protein design workflow.

Lead architecture and implementation of scalable web applications using NestJS and Next.js. Build and optimize PostgreSQL schemas and queries, implement responsive UIs with Tailwind CSS, integrate LangChain-driven AI features, manage authentication with Clerk, and create end-to-end tests and automation with Puppeteer to ensure launch readiness and reliable user experiences.

Operate and scale production inference systems serving live, multi-tenant traffic. Own model rollouts, observability, capacity planning, incident response, reliability, failover, and cost optimization. Partner with research and inference teams to productionize serving techniques while maintaining system performance and resilience.

Own the vision, roadmap, customer discovery, and end-to-end delivery of OpenGov’s AI assistant platform. This hands-on role combines product ownership with software engineering, including architecture, TypeScript and React development, testing, deployment, UX implementation, technical direction, metric analysis, and customer-facing communication. The engineer will use AI-native tools while maintaining accountability for production quality, product decisions, and business outcomes.

Design, develop, and implement cloud applications and automated tests on Microsoft Azure using C#/.NET and full-stack technologies. Evaluate architecture, scalability, reliability, performance, and maintenance risks; make technical trade-offs; design comprehensive test coverage; troubleshoot issues; and deliver moderately complex features. The role collaborates with software engineering teams while working independently on larger assignments and using Agile and AI-assisted development practices.