Top Artificial Intelligence Software Engineer Jobs

Build internal fullstack products and human-robot interfaces for operating, monitoring, debugging, and deploying robots. Responsibilities include realtime video and robot-state systems, safety-focused UX, on-device and cloud architecture, fleet versioning, observability, workflow measurement, and direct user-driven product development. The role partners with researchers, operators, and roboticists across lab, warehouse, and field environments.

Architect and lead CMIS software stacks, host interoperability testing, manufacturing test orchestration, telemetry pipelines, customer SDKs, and developer tools. Ensure compliance with CMIS, OSFP, SFF-8024, OIF, and related standards. Responsibilities include regression testing with hyperscalers, ATE integration, yield analytics, firmware-to-cloud telemetry, release management, architecture documentation, and customer, manufacturing, and standards engagement. Travel up to 25% is required.

Own customer onboarding, Salesforce integrations, technical pilots, data troubleshooting, model performance analysis, and post-launch optimization. Partner with customers, engineering, product, and founders to improve adoption, tune LLM outputs, build onboarding tooling, and inform product roadmap decisions. The role combines software engineering, data science, customer engagement, and technical delivery to drive measurable revenue outcomes.

Design and maintain scalable services, APIs, distributed systems, and data pipelines for AI training data infrastructure. Own systems through design, implementation, testing, deployment, and monitoring. Lead architectural decisions, improve reliability and scalability, mentor engineers, and collaborate with AI/ML, product, and data operations teams. Contribute to CI/CD, containerization, DevOps, and cloud deployment practices.

Develop high-performance C++ services and libraries on Linux for real-time systems integrating AI, hardware interfaces, and sensor data. Optimize CPU, memory, I/O, concurrency, and build performance; debug multithreaded issues; write tests; apply sanitizers and static analysis; and contribute to CI pipelines and developer tooling.

Develop software using C# and Oracle or SQL databases, including PL/SQL or T-SQL. Preferred experience includes web services, WCF, Web API, and Silverlight. The interview process includes an initial phone interview followed by an in-person interview in Miamisburg, Ohio, or Jacksonville, Florida, depending on the candidate’s location.

Build custom software and AI solutions for financial firms. Engineers work directly with client engineering teams and subject-matter experts to understand problems, design tailored systems, and ship reliable applications across varied projects. The role requires strong ownership, adaptability across domains, attention to software quality, and the ability to communicate effectively with clients.

Build and operate low-latency, reliable platform infrastructure for AI model evaluation, including APIs, AI gateways, streaming systems, serving layers, observability, authentication, rate limiting, usage metering, and audit logging. Integrate multiple LLM providers, handle failures and response normalization, support enterprise requirements, and contribute across backend evaluation and leaderboard platforms. This hands-on role requires strong Go expertise and experience with distributed systems or developer-facing infrastructure.

Build and operate backend platform capabilities including REST APIs, SDKs, agent runtimes, multi-tenant ingestion, and GPU job orchestration. Design resilient services for cloud, customer VPC, on-premises, and edge deployments. Own observability, reliability, SLOs, debugging, and on-call operations while translating AI research into production infrastructure. Senior engineers also drive architecture and engineering standards.

Lead architecture and implementation of Together AI’s global GPU cloud infrastructure. Own GPU and network virtualization, in-data-center IaaS, distributed scheduling and management planes, fault-tolerant automation, and customer-facing production systems. Set technical direction across teams, mentor engineers, establish engineering standards, and build scalable services spanning Kubernetes, compute, storage, networking, and GPU infrastructure.