Senior Software Engineer – AI and Autonomous Driving
Indexed description
We're hiring a mid‑level Software Engineer to build production AI for autonomous vehicles. If you're passionate about deploying robust, high‑performance models that run on GPUs in real cars, we'd like to hear from you.
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.
- 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)
- 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.
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