Senior Software Engineer

Responsibilities

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.

Qualifications

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.

Bonus Qualifications

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.