Senior Software Engineer

• Design, implement, and maintain robust software in C++ and Python, that supports ML training, evaluation, and inference workflows. • Build and maintain ML tooling for dataset handling, experiment tracking, metrics computation, and offline/online analysis. • Enable model export and edge inference prototyping, including model packaging, runtime integration, and performance validation on embedded compute platforms. • Build diagnostics, monitoring, logging, and introspection tools that provide visibility into runtime end-to-end machine learning model behavior and help accelerate iteration. • Collaborate with ML researchers to translate experimental models into repeatable, production-ready pipelines. • Support CI and automation for training, evaluation, and inference workflows. • Partner with cross-functional teams to support software deployment and versioning, ensuring consistent behavior across environments. • Apply rigorous engineering best practices, including code review, documentation, and testing, to deliver robust and maintainable systems.

• Bachelor or master degree in Computer Science, Robotics, or a related field. • 10+ years of relevant software development experience, ideally in robotics, automotive, embedded systems, or distributed platforms. • Strong proficiency in modern C++ (C++14/17/20) and Python. • Familiarity with Linux systems programming (e.g., sockets, filesystems, threading) and real-time systems. • Experience building ML platforms, data pipelines, or distributed software systems and supporting machine learning training or inference pipelines. • Familiarity with ML frameworks (PyTorch, TensorFlow), model deployment tools (TensorRT, ONNX, TorchScript) and inference runtimes. • Familiarity with Linux-based development environments and production debugging. • Experience integrating and debugging complex software systems, ideally in robotic or automated driving platforms. • Proven ability to work hands-on and cross-functionally to solve real-world deployment issues. • Experience in automated driving, robotics, or simulation-based system testing. • Hands-on experience with embedded systems development, including work on platforms such as NVIDIA Jetson Orin, Qualcomm Snapdragon Ride, or similar automotive-grade SoCs. • Familiarity with container orchestration (Docker, Kubernetes), or orchestration tools for testing and deployment. • Experience working with distributed compute systems, large-scale data logging, or introspection frameworks. • Understanding of automotive software practices and standards (e.g., ISO 26262, safety-critical development). • Prior experience in fast-paced R&D environments bridging research and production.