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Machine Learning Engineer (Automotive Unit) - Job Opportunity at Simi Reality Motion Systems GmbH

Unterschleißheim, Germany
Full-time
Mid-level
Posted: April 30, 2025
Hybrid
EUR 65,000 - 85,000 annually based on Munich metro area market rates for ML engineers in automotive sector

Benefits

Flexible working hours with modern work-life balance focus
Hybrid work arrangement with partial remote options
Modern office environment in Unterschleißheim
Flat organizational hierarchy supporting rapid career growth
Collaborative team environment with industry experts

Key Responsibilities

Lead development and optimization of deep neural networks for automotive safety applications
Engineer ML solutions for vehicle interior monitoring systems
Drive model deployment on embedded automotive hardware platforms
Conduct comprehensive performance analysis and validation in real vehicle environments
Collaborate with cross-functional teams to enhance data collection and model optimization
Design and implement structured training and evaluation pipelines

Requirements

Education

Master's degree in Computer Science or related field

Experience

Prior ML project experience through academic or professional work

Required Skills

Deep learning and computer vision expertise Python programming with PyTorch/TensorFlow OpenCV implementation experience Neural network deployment on embedded systems Data distribution analysis Model optimization techniques Synthetic data generation knowledge
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Sauge AI Market Intelligence

Industry Trends

The automotive AI sector is experiencing rapid growth with increased focus on in-vehicle monitoring systems for safety applications. Regulatory changes in vehicle safety standards are driving demand for ML-powered monitoring solutions. There's a growing convergence between traditional automotive safety systems and advanced AI capabilities, creating unique opportunities for ML specialists.

Role Significance

Likely part of a specialized AI team of 5-8 engineers within larger R&D department
Mid to senior technical position with significant impact on core safety systems development

Key Projects

Development of next-generation occupant monitoring systems Implementation of embedded AI solutions for real-time safety applications Creation of synthetic data generation pipelines for model training Optimization of ML models for automotive-grade hardware

Success Factors

Deep understanding of automotive safety requirements and regulatory landscape Ability to optimize ML models for restricted hardware environments Strong collaboration skills with cross-functional teams Experience with real-world ML deployment challenges

Market Demand

High demand with strong growth trajectory due to increasing integration of AI systems in vehicle safety features and European automotive industry's focus on innovation

Important Skills

Critical Skills

Deep learning expertise with focus on computer vision applications Embedded systems optimization experience Strong Python development skills with ML framework proficiency

Beneficial Skills

Knowledge of automotive safety standards Experience with synthetic data generation Understanding of motion capture systems Model quantization and optimization techniques

Unique Aspects

Direct involvement in safety-critical AI systems development
Combination of traditional automotive safety with cutting-edge AI applications
Access to real vehicle testing and deployment scenarios
Work with industry-leading motion capture technology

Career Growth

2-3 years for senior positions, 4-5 years for technical leadership roles

Potential Next Roles

Senior ML Engineer Technical Lead - AI Systems Automotive AI Architect R&D Team Lead

Company Overview

Simi Reality Motion Systems GmbH

ZF LIFETEC subsidiary specializing in motion capture and safety systems, representing the technological innovation arm of a major automotive supplier

Strong position in automotive safety systems with growing focus on AI integration
Significant presence in German automotive sector with global market reach
Innovation-focused environment with emphasis on technical excellence and safety-critical development practices
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