Senior Machine Learning Engineer

📋 Job Overview

When people use our technology to \"bridge the gap\" between the physical and digital worlds, they don’t just capture reality - they create a new one. In this new reality, they are smarter, more productive, more streamlined, and more creative - because they have the digital foundation to build the world they want to live in. That’s what NavVis offers in all our products and services: the tools to not just map the world as it is, but to pave the way to a better future. To forge something new. Physical or digital, there is only one reality. And it’s the reality NavVis empowers people to build better.

🏢 About NavVis

NavVis is a technologically-focused, global leader in reality capture and digital factory solutions. Prominent manufacturers and laser scanning professionals around the world trust our ground-breaking technology, designed to capture and share the built environment as photorealistic digital twins. By bridging the physical and digital worlds, we’re helping organizations make smarter decisions and build more efficient, connected operations.

With over 300 people from around 60 nationalities and offices around the globe, we’re proud to be a truly international and diverse place to work. As a scaling company, we bring together the agility, innovation, and entrepreneurial mindset of a startup with the professionalism and reliability of an established enterprise — offering our team the best of both worlds.

You’ll be part of an open culture that values trust, collaboration, and transparency. We are proud of our environment where diverse talent can thrive and where feedback fosters individual growth and development. Do you share our passion for disruptive technology and want to be part of our dynamic journey? At NavVis, you’ll own your projects, bring innovative ideas to life, and have real opportunities to grow your career and be recognized for your impact.

🎯 The Role

As a Senior Machine Learning Engineer, you will identify promising advances, rigorously evaluate them on our data, and make the strongest approaches work reliably at scale. Your primary focus will be point clouds and images, and in the future may expand to Gaussian splats, 360° video, CAD, and IFC data. You will configure, train, and adapt models, then integrate successful solutions into NavVis IVION to deliver measurable customer value.

You will work with one of the world's largest collections of high-fidelity 3D point clouds and registered 360° imagery of real built environments, including construction sites, factories, manufacturing plants, and oil and gas facilities. Customers capture and upload around 500 million square meters of spatial data each year, including survey-grade point clouds with resolution as fine as 3 mm and dense registered imagery, and at petabyte scale every solution must balance quality, speed, memory use, robustness, and cost. This is a hands-on role at the intersection of computer vision, machine learning, and spatial data.

✅ Key Responsibilities

• Evaluate and integrate AI/ML models, systems, and frameworks for spatial data, primarily point cloud and image data, applying them to use cases such as object segmentation, semantic scene understanding, and generating derived artifacts such as a floor layout plan
• Own the practical details that determine whether a system works in production: understand its constraints, make sound architectural choices, configure it correctly, prepare and preprocess data, and optimize its performance on our datasets
• Design systematic evaluations using rigorous methodology so decisions are driven by evidence rather than impressions
• Diagnose, debug, and enhance ML systems when results fall short, applying a deeply analytical mindset to understand why a model behaves the way it does and how to improve it
• Adapt or fine-tune existing models to our data where it delivers meaningful value, focusing on applying and getting the most from the state of the art rather than from-scratch model development and training
• Work closely with your team of computer vision, computer graphics, data processing, and full-stack engineers to turn ML capabilities into valuable features our users can rely on

📌 Required Qualifications

• A Master's or PhD in a relevant field (equivalent practical experience is equally valued)
• Several years of experience applying machine learning to real-world problems (exceptional candidates with less experience will also be considered)
• Proven ability to adapt ML models and frameworks to real data, including data preparation, configuration, and performance optimization
• A rigorous, evidence-driven approach to evaluation and experimentation, paired with strong analytical and debugging skills to understand and improve complex ML systems
• Strong Python skills and experience with modern deep-learning frameworks such as PyTorch or TensorFlow (C++ experience is a plus)
• A solid foundation in machine learning, linear algebra, and geometry
• Experience with spatial data, especially point clouds, images, or video data (familiarity with relevant architectures such as point or vision transformers, Segment Anything models, or Grounding DINO/CLIP is a plus)

🎁 Benefits

• 30 days of paid time off per year
• Affordable access to a vast network of fitness and wellness facilities through EGYM Wellpass subsidy
• Deutschlandticket subsidy to support sustainable travel using public transport
• Flexible working hours and a hybrid work setup, enabling you to plan your work around your life, and not your life around work!
• Full visa and relocation support for international candidates
• An attractive bike leasing model through JobRad, in line with our commitment towards sustainable mobility
• A competitive compensation package that values the skills and experience you bring
• Up to 4000 EUR employee referral bonus
• Financial support for local language classes to help you in your journey of integrating into the culture!

🛂 Visa & Eligibility

Full visa and relocation support for international candidates. Financial support for local language classes available.