Edge AI Engineer (Mid/Senior) | Telematics

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MidHybryda·WarszawaFull-time

#474649·Dodano wczoraj·1

Źródło: Teltonika

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Tech Stack / Keywords

Python PyTorch TensorFlowTensorFlow LiteONNX RuntimeTensorRTC/C++LinuxGStreamerV4L2

Podsumowanie oferty

Join an R&D team developing video telematics devices. You will evaluate, optimize, and deploy machine learning models for edge devices, PC validation, and future cloud processing, working with technologies including 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 and edge hardware, including CPU/GPU/NPU limitations, memory, and power.
Comfortable working in 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.

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 embedded edge devices and PC platforms running Windows or Linux.
Analyze system capabilities, including CPU, GPU, NPU, memory, and power budget, and contribute to selecting hardware specifications 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 preprocessing and future cloud-based models that process data collected from edge AI devices.
Collaborate with embedded software and hardware teams to integrate models into the media and 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.
An additional day off for your birthday.
Team-building activities.

Opieka zdrowotna

Płatny urlop

Spotkania integracyjne

Teltonika

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