Edge AI Engineer – Mid/Senior

Teltonika

. Analyze, select, and evaluate machine learning models for specific use cases and problem-solving needs, including ADAS/DMS, video/image analysis, and sensor and CAN data processing .

Posted 11/5/2026full-timeWarsaw • PolandSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI/ML model development and deployment, particularly for edge devices, with strong proficiency in Python and relevant ML frameworks. Capable of optimizing models for resource-constrained environments and collaborating effectively with cross-functional teams.

Highest-signal resume keywords

AI/ML Model DevelopmentPython ProgrammingEmbedded Systems IntegrationEdge Inference OptimizationComputer Vision Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel EvaluationModel BenchmarkingQuantizationPruningTensorFlow LiteONNX RuntimeC/C++ ProgrammingCloud ML InfrastructureData Analysis

Soft Skills

Analytical SkillsProblem SolvingCollaboration

Tools & Technologies

LinuxWindowsV4L2GStreamerMLOps PipelinesNPU SDKs

Industry Keywords

ADASDMSVideo/Image AnalysisEmbedded Edge DevicesAI Workloads

Tech Stack

Tools & technologies

CloudLinuxPythonPyTorchTensorflowC++

About the role

Key responsibilities & impact

Analyze, select, and evaluate machine learning models for specific use cases and problem-solving needs, including ADAS/DMS, video/image analysis, and sensor and CAN data processing
Test, benchmark, and configure AI models for deployment on embedded edge devices and PC platforms running Windows and 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

Requirements

What you’ll need

2+ years of experience in AI/ML model development, evaluation, or deployment
Solid Python skills and practical experience with ML frameworks (PyTorch, TensorFlow, or similar)
Experience in testing and benchmarking models for accuracy, latency, and resource usage
Understanding of the constraints of running inference on embedded/edge hardware (CPU/GPU/NPU limitations, memory, power)
Comfortable working across both Linux and Windows environments
Strong analytical skills and the ability to independently investigate and solve technical problems
Professional working proficiency in English
Experience with edge inference runtimes and toolchains (TensorFlow Lite, ONNX Runtime, TensorRT, or vendor NPU SDKs such as NXP, Qualcomm, Mediatek, Rockchip RKNN, or similar)
Experience with computer vision models relevant to ADAS/DMS (object detection, driver monitoring, event classification)
Experience with C/C++ for integrating models into embedded applications
Familiarity with embedded Linux and camera/video pipelines (V4L2, GStreamer)
Experience with cloud ML infrastructure (model serving, MLOps pipelines, containerized inference)
Bachelor's degree in Computer Science, Data Science, Electronics Engineering, or a related field (ideally)

Benefits

Comp & perks

Private medical care (Medicover Premium) and a MultiBenefit cafeteria
Unique opportunities to grow professionally being part of a challenging and high-growth environment
Interesting and ambitious projects of industrial GPS tracking devices
Additional day off for your birthday
Team building activities