Senior AI Systems Engineer – Material Intelligence
At a glance
Senior AI Engineering role at NETZSCH Group. Florianóplis · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Prepare structured material data for machine-learning work and refine the pipelines, features and representations behind it
- Help build models that predict material properties, then test their robustness, generalization and uncertainty
- Set internal practices for validating, explaining and reproducing models, and record key design choices and limitations
- Work with partners and internal stakeholders to turn AI findings into insights relevant to material science
- Develop scalable material-data systems and practical AI tools, including lightweight agents and LLM-supported workflow automation
What you bring
- You have applied machine learning in real settings, especially with structured or industrial data
- You program well and have worked on data preparation and pipeline development
- You understand feature design, model testing and the challenges of small or complex datasets
- You can assess model assumptions, limits and reliability, and communicate in advanced English
Who this fits
This senior role suits someone who wants to build internal capability for AI systems using material data, with work spanning modeling, infrastructure and documentation. Hybrid work is preferred in Florianópolis or Curitiba, and the contract can be CLT or PJ.
From the employer
NEDGEX is an innovation unit focused on building and scaling new digital and AI-driven ventures. We work on high-impact, forward-looking initiatives across industries, combining strong engineering with applied AI.
In this role, you will primarily contribute to MaterialIQ, one of our key innovation projects. MaterialIQ focuses on developing AI systems based on material datasets to unlock deeper insights into material properties and behavior.
Your primary mission is to build internal AI and data science capability for material intelligence systems. You will work with structured, complex, and often limited material datasets and help develop robust, interpretable, and production-relevant models.
At the same time, you will ensure that knowledge about data pipelines, feature engineering, model assumptions, and validation strategies is understood, documented, and internalized.
Responsibilities:
• Work with structured material datasets and prepare them for AI and machine learning applications.
• Analyze and improve data pipelines, feature engineering approaches, and data representations.
• Support the development of predictive models for material properties and related use cases.
• Evaluate model performance with a strong focus on robustness, generalization, and uncertainty.
• Develop internal standards for model validation, interpretability, and reproducibility.
• Document system architectures, modeling decisions, assumptions, and limitations in a structured way.
• Collaborate closely with external partners and internal stakeholders to deeply understand AI systems.
• Translate AI and data science concepts into material-science-relevant insights.
• Contribute to building a scalable architecture for AI-enabled material data systems.
• Build and integrate AI-based tools and lightweight agent systems to improve workflows.
• Use LLMs pragmatically for automation, data processing, and developer support.
• Contribute to AI infrastructure and tooling design decisions.
• Ensure systems are reliable, maintainable, and usable in practice.
Your Profile
- Advanced English;
- Strong programming skills;
- Experience with machine learning in real-world settings, especially with structured or industrial datasets;
- Solid understanding of:
- Feature engineering
- Model validation
- Handling small or complex datasets
- Ability to reason about model assumptions, limitations, and robustness;
- Experience with data pipelines and data preparation;
- Familiarity with modern AI tooling (LLMs, APIs, basic automation) — deep specialization in agent systems is not required;
- Exposure to scientific, engineering, or physics-related data is a strong plus.
📍 CLT or PJ contract
🏡 Preferably hybrid work in Florianópolis or Curitiba.