Architect and implement core platform components, including robotics SDKs, cloud-hosted Application Programming Interfaces (APIs), edge runtimes, and agent orchestration frameworks that enable developers and partners to compose interoperable autonomy capabilities (perception, planning, control, multi-agent coordination) into deployable mission workflows. Design the platform's extensibility and integration architecture, defining how first-party autonomy capabilities, first- and third-party models, partner hardware systems, and customer-specific logic are composed, versioned, tested, and deployed across cloud and edge environments. Build production-grade data infrastructure spanning the full robotics lifecycle including instrumentation libraries, data acquisition services, human-in-the-loop workflows, dataset versioning and curation pipelines, and data quality governance supporting both real-world and synthetic/simulated data at scale. Own cross-cutting platform concerns including authentication and authorization across cloud-edge boundaries, API versioning and backward compatibility, multi-tenant isolation, and performance at the latencies required by real-time robotic control loops. Collaborate with autonomy, simulation, and evaluation teams to ensure that platform primitives (compute orchestration, data routing, model serving, experiment tracking) meet the performance, reliability, and reproducibility requirements of Machine Learning (ML) training, sim-to-real transfer, and online evaluation workloads. Lead technical design reviews, write architecture decision records, and establish engineering practices for the platform team, mentoring senior engineers and raising the bar for code quality, testing, and operational readiness across the organization. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 6+ years of experience designing and building developer platforms, SDKs, APIs, or distributed infrastructure used by multiple teams or external partners, with demonstrable ownership of cross-team technical decisions and architecture. Deep expertise in cloud-native systems engineering - including microservice architecture, containerized workloads (Kubernetes), service mesh, API gateway patterns, and CI/CD at scale - preferably on Azure. Proficiency in C++, Rust, or Go for systems-level components AND Python for tooling, data pipelines, and ML integration, with high standards for code quality, testing, and performance. Demonstrated ability to make and communicate architectural trade-offs across competing concerns (latency vs. throughput, flexibility vs. safety, developer velocity vs. operational complexity), with a track record of decisions that held up at scale. Experience with robotics middleware (Robot Operating System (ROS)/ROS2), real-time systems, or edge computing platforms, including the unique constraints of deploying software to physically embodied systems with strict latency and safety requirements. Familiarity with ML infrastructure including model serving (Open Neural Network Exchange (ONNX), TensorRT, Triton), training orchestration, experiment tracking, or feature stores, particularly in the context of deploying learned models to edge devices. Experience building simulation infrastructure, digital twin platforms, or CI-based testing pipelines that incorporate physics-based simulation for validation and regression testing. Prior work designing multi-tenant platforms with partner-facing APIs, including developer portal experiences, API versioning strategies, and backward-compatibility guarantees. Understanding of agentic AI architectures, including agent orchestration, tool use, multi-agent coordination, and human-in-the-loop workflows, and how they apply to physical systems. Contributions to open-source robotics, cloud, or developer tooling projects, or published work on platform architecture, robotics systems, or distributed systems.