Staff Software ML Engineer

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore.

For more details visit: www.netradyne.com

Job Responsibilities As a Staff Software ML Engineer in System Optimization, you will implement, and evolve processes to ensure the efficient and high-performance operation of our in-house designed and developed driver monitoring and assistance technology. Your responsibilities will include:

1. Crafting and developing tools, frameworks, and reporting mechanisms for system optimization.
2. Streamlining software for deployment across IoT devices, implementing GPU/CPU/DSPs.
3. Enhancing data platforms and optimizing machine learning models deployed on the platform.
4. Improving driver monitoring and driver assistance algorithms to boost system efficiency and report key performance indicators (KPIs).
5. Managing production inquiries and ensuring overall application stability in production environments.
6. Effectively conveying highly technical results to diverse audiences.

Requirements To excel in this Staff Software ML Engineer in System Optimization role, you should possess the following qualifications:

1. Hold a B.E/B.Tech or M.E/M.Tech degree with a minimum of 6+ years of experience in software system optimization.
2. Exhibit exceptional attention to detail, strong analytical skills, and a creative mindset dedicated to achieving optimal system performance.
3. Demonstrate proficiency in programming languages such as C/C++, OpenGL, CUDA, and Python.
4. Possess a solid grasp of basic statistics, probability, and concepts in machine learning (ML) and computer vision (CV).
5. Have experience with ML frameworks, including Caffe, TensorRT, OpenCL, SNPE, OpenVino, and ONNX.
6. Bring experience in embedded platforms, make files, build systems, and familiarity with Jenkins, which is a valuable asset.