Top Artificial Intelligence Software Engineer Jobs

Lead development of high-performance Rust parsers, compilers, automata, indexes, and data structures for structured language-model generation. Optimize decoding-loop performance through profiling and benchmarking, integrate Python bindings and C++/CUDA components into inference engines, collaborate across teams, and maintain production-quality code through testing and CI/CD.

Lead enterprise MuleSoft and API management initiatives by designing, implementing, securing, deploying, and governing integrations across SAP, ERP, cloud, on-premise, hybrid, SaaS, and legacy environments. Develop API specifications, layered API architectures, integration flows, and technical diagrams. Provide technical leadership, mentorship, and architectural guidance to distributed teams while ensuring scalable solutions and adherence to integration best practices.

Develop C++ checkers and defect-fix algorithms for a static code analysis tool. Contribute to core source-code analysis and transformation algorithms, participate in daily Scrum meetings, and help advance AI-enabled software quality products.

Develop real-time perception software for SLAM and visual-inertial odometry by fusing camera and IMU data. Build structure-from-motion, sensor calibration, synchronization, and 3D reconstruction pipelines; evaluate algorithms on benchmarks and real-world datasets; and integrate perception outputs into autonomy and collaborative mapping systems. Collaborate with hardware, robotics, and platform teams to deliver production-ready systems on embedded and edge devices, including metrics, testing frameworks, optimization, and deployment strategies.

Translate Python code into C++ for API integration, parse brain-computer interface data, and support research and development through testing and refinement of Unity applications. The role also involves designing, building, testing, and maintaining reliable C++ code, improving code quality and automation, rapidly prototyping features, and documenting technical work in a fast-paced startup environment.

Develop software infrastructure for AI-era data management at VAST Data. The role is designed for a new college graduate and involves writing clean, documented code using C, C++, or Python, applying data structures and algorithms, communicating effectively, and learning through mentorship from senior engineers.

Owns the wireless software stack for a distributed mesh sensor network, including radio configuration, telemetry, diagnostics, chipset integration, fleet tooling, RF simulation, and field deployment support. The role requires debugging across radio, driver, kernel, embedded, and application layers while collaborating with RF, hardware, embedded, and backend teams to improve wireless performance, video quality, and reliability.

Develop and integrate software across a robotics stack, including real-time perception, control, firmware, and embedded systems. Deliver reliable, performant code; contribute to architecture, testing, feature rollout, diagnostics, and lifecycle development. Lead prototype robot integration, bring-up, debugging, and engineering tests while collaborating across hardware and software teams. Required expertise includes Python, system-level C/C++/Rust, concurrency, robotics control, network communications, Git, and maintainable software design. Computer vision, deep learning, Linux, and production perception experience are preferred.

Own the architecture and evolution of Osmo’s canonical data platform, including data models, schemas, migrations, replication, APIs, integrations, access control, validation, concurrency, and auditability. Lead consistency across distributed systems under retries and partial failures, establish data modeling standards, partner with scientific and technical experts, and improve team capability through durable engineering practices and mentorship.

Build end-to-end product experiences supporting expert acquisition, onboarding, assessment, matching, activation, and retention. Partner with Growth and Operations to identify bottlenecks, instrument funnels, run experiments, and improve measurable outcomes. Develop internal tools and automation, create durable product primitives, and make thoughtful tradeoffs among speed, reliability, and maintainability while working closely with founders and cross-functional teams.