Senior Software Engineer – AI and Autonomous Driving

Requirements:

• 4–8 years of professional software engineering experience, ideally in AI, robotics, or automotive domains.
• Proficiency in C++ (modern C++14/17 or later) and Python, with demonstrated experience writing clean, maintainable code.
• Hands-on experience training deep learning models (PyTorch or TensorFlow): designing experiments, tuning hyperparameters, working with large datasets, and debugging model behavior.
• Strong Linux development skills: building, debugging, profiling, version control (git), and working within CI/CD workflows.
• Familiarity with one or more of:
  • GPU programming and optimization (CUDA, TensorRT, cuDNN)
  • Computer vision and perception (object detection, segmentation, multi-object tracking)
  • Robotics or autonomous systems (ROS, ADAS features, simulation environments)

Ways to stand out from the crowd:

• Experience with camera calibration, sensor fusion, or multi-camera perception systems.
• Knowledge of model optimization and deployment: quantization (INT8, FP8, 4-bit), TensorRT-LLM, ONNX Runtime, or similar frameworks.
• Background in training infrastructure: distributed training, experiment tracking, dataset versioning, hyperparameter optimization.
• Understanding of software quality practices for safety-critical systems (code review, unit testing, static analysis; automotive standards knowledge is a plus).
• Open-source contributions or published work in AI, robotics, or GPU computing.

What you’ll be doing:

• Design, develop, and maintain C++ and Python software for perception, prediction, and planning in advanced driver-assistance and autonomous driving systems.
• Train, fine-tune, and iterate on deep learning models (vision, multimodal, and transformer-based architectures) using large-scale driving datasets, then optimize them for real-time inference on NVIDIA GPUs.
• Work with multi-sensor data — cameras, radar, lidar — and contribute to training pipelines, data quality workflows, and automated evaluation infrastructure.
• Debug and resolve performance bottlenecks, edge cases, and integration challenges in a complex, safety-critical codebase.
• Collaborate with ML researchers, systems engineers, and automotive partners to bring features from research prototypes to production-ready systems.