Edge AI Engineer (Mid/Senior) | Telematics

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MidHybryda·WarszawaFull-time·Umowa o pracę

#474784·Dodano wczoraj·1

Źródło: justjoin.it

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Tech Stack / Keywords

Machine Learning Python C C++PyTorch TensorFlow

Podsumowanie oferty

Join the R&D team developing next-generation video telematics devices. You will evaluate and deploy machine learning models for edge inference and device data processing, using Python, PyTorch and TensorFlow.

Wymagania

2+ years of experience in AI/ML model development, evaluation, or deployment.
Solid Python skills and practical experience with ML frameworks such as PyTorch or TensorFlow.
Experience testing and benchmarking models for accuracy, latency, and resource usage.
Understanding of the constraints of running inference on embedded/edge hardware, including CPU, GPU, and NPU limitations, memory, and power.
Comfortable working across Linux and Windows environments.
Strong analytical skills and the ability to independently investigate and solve technical problems.
Professional working proficiency in English.

Nice to have:

Experience with edge inference runtimes and toolchains such as TensorFlow Lite, ONNX Runtime, TensorRT, or vendor NPU SDKs such as NXP, Qualcomm, MediaTek, or Rockchip RKNN.
Experience with computer vision models relevant to ADAS/DMS, including object detection, driver monitoring, and event classification.
Experience with C/C++ for integrating models into embedded applications.
Familiarity with embedded Linux and camera/video pipelines such as V4L2 and GStreamer.
Experience with cloud ML infrastructure, including model serving, MLOps pipelines, and containerized inference.
Bachelor's degree in Computer Science, Data Science, Electronics Engineering, or a related field.
Any other relevant experience that you think makes you a great fit.

Obowiązki

Analyze, select, and evaluate machine learning models for use cases such as ADAS/DMS, video and image analysis, and sensor and CAN data processing.
Test, benchmark, and configure AI models for deployment on embedded edge devices and on PC platforms running Windows or Linux.
Analyze system capabilities, including CPU, GPU, NPU, memory, and power budget, and contribute to selecting appropriate hardware for AI workloads.
Optimize models for resource-constrained edge hardware through quantization, pruning, and conversion to inference runtimes such as TensorFlow Lite, ONNX Runtime, or vendor-specific SDKs.
Design data flow between edge-level pre-processing and future cloud-based models processing data collected from edge AI devices.
Collaborate with embedded software and hardware teams to integrate models into the media/data pipeline of video telematics devices.
Track current and emerging AI/ML trends and evaluate their applicability to company products.
Document model performance, testing methodology, and deployment decisions.
Participate in Agile/Scrum team processes and decision making.

Benefity

Private medical care (Medicover Premium) and a MultiBenefit cafeteria.
Additional day off for your birthday.
Team building activities.

Opieka zdrowotna

Płatny urlop

Spotkania integracyjne

Teltonika Poland

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