Implement and test features within core platform components with guidance, 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. Contribute to platform extensibility and integration features using established patterns, helping integrate, version, test, and deploy first-party autonomy capabilities, first- and third-party models, partner hardware systems, and customer-specific logic across cloud and edge environments. Develop and test components of data infrastructure for the robotics lifecycle, such as instrumentation libraries, data acquisition services, human-in-the-loop workflows, and dataset versioning and curation pipelines, applying established data quality practices to real-world and synthetic/simulated data with guidance from the team. Apply established security, compatibility, and performance practices to assigned features with guidance, including authentication and authorization across cloud-edge boundaries, API versioning and backward compatibility, and multi-tenant isolation. Help test and investigate performance against the latency requirements of real-time robotic control loops. Use AI-assisted development tools, such as GitHub Copilot, and agentic coding workflows to improve engineering productivity and software quality. Contribute to agent-based engineering solutions that automate repetitive tasks, accelerate investigation and root cause analysis, and strengthen testing, validation, and operational reliability, applying team standards for code review and verification of AI-generated changes. Collaborate with autonomy, simulation, and evaluation teams to understand requirements and implement and test assigned features in platform primitives (compute orchestration, data routing, model serving, experiment tracking), helping support the performance, reliability, and reproducibility needs of Machine Learning (ML) training, sim-to-real transfer, and online evaluation workloads. Participate in technical design and code reviews, contribute to design documentation for assigned features, and incorporate feedback from experienced engineers. Apply team standards for code quality, testing, and operational readiness, and communicate progress, questions, and blockers. Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include, but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Experience contributing to software projects involving developer tools, SDKs, APIs, or distributed systems through coursework, personal projects, internships, research, open-source contributions, or professional work. Experience coding in one or more of C++, Rust, Go, or Python, with an interest in systems-level components, tooling, data pipelines, or ML integration and an emphasis on clear, maintainable, testable code. Experience using AI-assisted development tools, such as GitHub Copilot, including exposure to agentic coding workflows, with an emphasis on reviewing, testing, and validating AI-generated code. Ability to compare and communicate the strengths and weaknesses of design options, considering factors such as latency, reliability, safety, and maintainability, and to incorporate feedback from others. Familiarity with cloud services and distributed systems concepts, such as microservices, containerized workloads (e.g., Kubernetes), APIs, or CI/CD; exposure to Azure is a plus. Familiarity with API design and integration concepts, such as versioning and backward compatibility, and an interest in learning how multi-tenant platforms support internal developers and external partners. Familiarity with robotics middleware (ROS/ROS2), real-time systems, or edge computing, and an interest in learning how latency, reliability, and safety requirements influence software deployed to physical systems. 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. Familiarity with ML infrastructure including model serving (ONNX, TensorRT, Triton), training orchestration, experiment tracking, or feature stores, particularly in the context of deploying learned models to edge devices. Exposure to simulation tools, digital twins, or automated testing, with an interest in using physics-based simulation to validate robotics software and catch regressions.
