Machine Learning Engineer

Bosch Engineering GmbH

. Identify opportunities for new DSP/ML algorithms by understanding device constraints, sensor characteristics, and hardware capabilities .

Posted 10/31/2026full-timeVienna • AustriaMid-LevelSenior💰 €68,000 - €75,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing machine learning models for embedded and edge AI applications, with strong proficiency in C/C++ and Python. Capable of designing end-to-end embedded AI systems and integrating ML inference into real-time firmware pipelines.

Highest-signal resume keywords

C/C++ ProficiencyMachine Learning Model DevelopmentEmbedded Systems OptimizationReal-Time Inference PipelinesML Compilers/Frameworks Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningEmbedded SystemsModel EvaluationQuantizationFixed-Point InferenceAudio DSPSensor Data ProcessingLow-Latency ProcessingCross-Compilation ToolchainsPerformance Profiling

Soft Skills

CollaborationFeedback Provision

Tools & Technologies

FreeRTOSTFLite MicroExecuTorchCMakeMLIRGlow

Certifications & Qualifications

Master’s or Ph.D. in Computer ScienceElectrical EngineeringMachine Learning

Industry Keywords

Embedded AIMultimodal ProcessingReal-Time FirmwareAudio ProcessingTinyML

Tech Stack

Tools & technologies

PythonRTOSC++

About the role

Key responsibilities & impact

Identify opportunities for new DSP/ML algorithms by understanding device constraints, sensor characteristics, and hardware capabilities
Develop audio and multimodal ML models for embedded and edge AI applications
Design, train, evaluate, and iterate on models for real-world sensing and interaction use cases
Prototype novel approaches for low-latency, on-device audio and multimodal processing
Develop software for RTOS environments such as FreeRTOS
Deploy models to device runtimes and hardware accelerators including DSP, NPU, and MCU
Convert trained ML models into efficient embedded implementations using C/C++, quantization, and fixed-point inference
Optimize memory footprint, SRAM usage, latency, and power consumption
Integrate ML inference into real-time firmware pipelines
Design end-to-end embedded AI systems from sensor through preprocessing, model, and postprocessing
Architect reusable embedded ML platform components across multiple hardware targets
Profile and optimize performance on MCUs, DSP cores, NPUs, and custom accelerators
Work with cross-compilation toolchains, CMake-based builds, and modular codebases
Integrate with on-device ML runtimes such as TFLite Micro, ExecuTorch, and custom interpreters
Provide feedback on model architecture for deployment efficiency and real-time behavior
Collaborate with ML researchers, DSP experts, firmware engineers, and hardware teams

Requirements

What you’ll need

Strong proficiency in C/C++ for embedded systems
Strong experience developing and evaluating machine learning models, preferably for audio, speech, or other time-series sensor data
Experience optimizing ML models for edge, embedded, or resource-constrained environments
Proficiency in Python for model development, experimentation, evaluation, and tooling
Master’s or Ph.D. in Computer Science, Electrical Engineering, Machine Learning or related field (preferred)
Experience with ML compilers/frameworks such as MLIR, Glow, ExecuTorch
Experience with real-time streaming inference pipelines
Knowledge of acoustics and classical audio DSP
Experience with on-device ML (TinyML, quantization, pruning)
Publication track record in ML, DSP, systems, or embedded AI