Machine Learning Engineer (eBike) Job at Bosch | Hybrid Portugal

📍 Location: eu

🏷 Type: Not specified

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Table of Contents

Job Overview

This Machine Learning Engineer role at Bosch Group focuses on building intelligent systems within the mobility and eBike technology domain. Designed for a Mid-Level (2–5 years) professional, the position blends ML model development, deployment, and real-world optimization. Ideal candidates combine strong mathematical foundations with hands-on experience in Python ML ecosystems. You will collaborate across engineering, hardware, and business teams to deliver scalable, production-grade solutions with measurable business impact.

🌍 Work Eligibility & Location

🌍 Visa Sponsorship: Not explicitly stated
✈️ Relocation Support: Not specified
🏠 Remote Type: Hybrid
⏰ Timezone Requirement: CET-compatible collaboration
🌐 Country Restrictions: EU hiring preference likely
🗣️ Language Requirement: English (fluent)

This is a Hybrid opportunity based in Portugal, requiring partial on-site presence in Braga. While visa sponsorship is not clearly confirmed, international candidates with EU work authorization will have a smoother pathway. The role operates in a global engineering environment, so alignment with European working hours is essential.

💰 Salary Intelligence

💰 Official Salary: Not disclosed
📊 Estimated Range: €35,000 – €55,000/year
📈 Level: Mid-Level

Although Bosch has not published a salary band, market benchmarks for Machine Learning Engineers in Portugal suggest a range between €35K and €55K annually. Given the global brand reputation and benefits package, compensation is considered competitive for mid-tier EU markets, especially when factoring in hybrid flexibility and career growth.

📊 Role Breakdown

This role balances data science experimentation with production-grade machine learning engineering. Approximately 40% of your time will involve designing and optimizing ML models using frameworks like PyTorch and TensorFlow. Another 25% focuses on data preprocessing, signal processing techniques, and feature engineering using tools like NumPy, Pandas, and SciPy. Around 20% is dedicated to deployment and monitoring, leveraging tools such as MLflow and DVC to ensure model reliability and business impact. The remaining 15% includes cross-functional collaboration with software, hardware, and business stakeholders. The emphasis is on end-to-end ownership, from identifying business problems to delivering measurable outcomes through scalable ML systems.

🧩 Required Skills & Fit

✅ Must: Strong experience with Python and ML libraries (PyTorch, TensorFlow, Scikit-learn)
✅ Must: Deep understanding of statistics, probability, and algorithms
✅ Must: Experience in ML model development, evaluation, and deployment
➕ Bonus: Knowledge of signal processing techniques
➕ Bonus: Experience with ML lifecycle tools like MLflow or DVC

📈 Difficulty & Competitiveness

⚡ Level: Moderate–High
📊 Experience barrier: 2–5 years
🧠 Skill complexity: High (math + engineering blend)
🌍 Competition: High across EU talent pool

This role sits at a Moderate–High difficulty level due to its expectation of both theoretical depth and practical ML deployment skills. Candidates with 2–5 years of experience must demonstrate real-world impact, not just academic exposure. Competition is strong, particularly from EU-based ML engineers with production experience.

🚀 Career Impact

⭐ Impact Rating: ⭐⭐⭐⭐☆
🏢 Brand value: Strong global engineering leader
📚 Skill growth: End-to-end ML systems exposure
🚀 Future opportunities: Senior ML, AI Architect roles

Joining Bosch significantly strengthens your career credibility in the AI and mobility space. You gain exposure to real-world ML deployment, positioning you for senior engineering roles or transitions into AI system architecture. The combination of hardware + software integration is especially valuable in emerging AI-driven industries.

📋 Key Responsibilities

You will identify business problems and translate them into data-driven ML solutions. Core tasks include designing, training, and optimizing models using PyTorch, TensorFlow, and Scikit-learn. You will process and transform datasets with tools like Pandas and NumPy, while applying signal processing techniques when needed. A critical part of the role is to deploy and monitor models using MLflow and versioning systems like Git. Additionally, you will collaborate cross-functionally with hardware engineers, software developers, and business stakeholders to ensure solutions deliver measurable business impact.

🎯 Application Strategy

🎯 Best apply method: Apply via official Bosch careers portal
🔥 Highlight: Production ML deployment experience
🔥 Highlight: Strong mathematical problem-solving
❌ Avoid: Overly academic CV without real-world impact
❌ Avoid: Generic project descriptions lacking metrics

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

How to use: Paste into ChatGPT, Claude, or Gemini

You are a senior technical recruiter.

Role:
Machine Learning Engineer focused on eBike systems, involving ML model development, deployment, and cross-functional collaboration in a hybrid EU environment.

Candidate:
[Paste CV]

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Focus:

Signal strength
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Missing high-impact elements

🧠 Fit & Positioning Analysis

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Act as a hiring panel.

Evaluate:

Match score
Strengths
Gaps
Positioning improvements

📅 Application Signals

This opportunity shows moderate urgency as Bosch typically hires on rolling cycles, but strong candidates are shortlisted quickly. Expect high competition from EU-based ML engineers with hands-on deployment experience. Early application combined with a tailored CV significantly improves your chances of progressing to interview stages.

🚀 Resume Optimization for This Role

⚡ Takes less than 2 minutes — optimize specifically for this position before applying

🎯 0/5 completed

Tailoring your CV to this exact role significantly increases your chances.

Model Deployment: Showcase deploying ML models using MLflow, DVC, Git pipelines

Framework Expertise: Highlight PyTorch, TensorFlow, Scikit-learn production model experience

Signal Processing: Demonstrate filtering, resampling applied to real-world sensor data

Business Impact: Quantify model improvements with measurable performance or efficiency gains

Cross-Functional Work: Emphasize collaboration with hardware, software, and business stakeholders

✅ Ready to apply — your profile is aligned with this role

Next Step: Tailor your CV to this role and apply through the official page below

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✅ Job Source & Verification

This job is sourced directly from the Bosch Group careers platform, ensuring high source credibility and authenticity. The listing reflects an active hiring need within Bosch Service Solutions. Last updated: recent posting timeframe based on current availability, though candidates should verify status on the official careers page before applying.