EnglishWillDo

(Senior) ML Engineer - Computer Vision (f/m/d)

Credium · Augsburg, Germany

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You aren't just looking for a job; you're looking for a stage for your entrepreneurial mindset and your drive to get things moving—and you want to be part of an ambitious team aiming to achieve great things together? Then we should talk.

At credium, we turn aerial imagery, LiDAR and 3D geodata into building intelligence – data that helps make property verification digital, reliable and accessible. Computer vision is one of the technologies that makes this possible, and we are looking for a Machine Learning Engineer who wants to take this topic forward together with us.

You will own our vision data products end-to-end and have a real say in where our AI journey goes next: which models we build ourselves, where modern platforms and foundation models get us further, and how we bring all of it into dependable production. The technical direction is shaped by the people doing the work – and that will include you from day one.

Why this role matters

Vision is an important part of our core data products, and it is one part of a genuinely varied role. Your time will be spent on vision topics, ML and data engineering, MLOps and data quality across our products. If you enjoy seeing the whole path from raw data to a product customers rely on, and you would rather build something lean and durable than reinvent it every quarter, you will feel at home here.

What you'll do

  • Own the lifecycle of our productive vision models end-to-end: training playbooks, evaluation with gold sets and per-class metrics, versioning, release gates and drift monitoring
  • Harden and operate our batch inference and geospatial postprocessing pipelines on Azure ML, treating every inference run as a controlled, cost-aware data product release
  • Work with 3D vision data – point clouds (LiDAR), meshes and 3D building geometries – and help us combine 2D imagery with 3D data into richer building intelligence
  • Steer our labeling operations together with our experienced external labeling team: you define specifications, QA sampling and acceptance criteria and empower the team to deliver great data quality
  • Shape our broader AI direction, including the use of agentic AI and automation to keep our processes lean and our team focused
  • Collaborate closely with our data engineers, data scientists and product manager, sharing knowledge openly so vision expertise grows across the whole team

This role is perfect for someone who thrives on responsibility, loves building clear processes and playbooks, and takes pride in making AI reliable, sustainable and valuable in production.#### What we’re looking for

Don’t worry if you don’t fulfill all criteria. Everyone’s got a focus.

  • You hold a degree or equal experience in Computer Science, Data Science/Engineering, Geoinformatics, Remote Sensing or a related field, or comparable working experience
  • You have several years of professional experience bringing ML systems into production (MLOps, CI/CD, monitoring), ideally on Azure
  • You have hands-on experience with computer vision in production – you can evaluate, fine-tune and operate models confidently
  • You have working knowledge of 3D vision data: processing point clouds (e.g. LiDAR, LAS/LAZ), meshes, Gaussian Splats, Voxels, and 3D geometries with tools such as PDAL, Open3D, trimesh or PyVista – and ideally first experience with 3D data formats like CityGML or 3D Tiles
  • You have experience with geospatial data and aerial imagery (PostGIS, QGIS, raster/vector processing) or with photogrammetry and 3D reconstruction and know what georeferencing is
  • You possess strong Python and solid data engineering skills (pipelines, batch processing, online inference, data quality checks, CI/CD)
  • You have practical experience with modern AI tooling: foundation models, auto-labeling, vision platforms (e.g. Roboflow) – experience with agentic AI workflows is a strong plus
  • You think pragmatically and economically: you enjoy weighing build-vs-buy options and are happy to choose open models, platforms or existing data when that is the smarter path for the product
  • You have experience with labeling operations and data quality assurance, or the structured mindset to set them up
  • You communicate clearly, document well and enjoy sharing your knowledge and enabling others (working students, labeling team, colleagues)
  • You are highly motivated, hands-on and comfortable with the pace, ambiguity and ownership of a startup environment
  • (Bonus) Knowledge of IT security principles in the context of data handling

How we work

We’re not building a lifestyle business. We’re building a company that matters.

We're an AI-first team, using machine learning and intelligent systems across our workflows - from building data extraction to product recommendations.

We believe in high trust, high standards, and high impact.

We support flexibility, but we don’t optimize for 9-to-5. Instead, we look for individuals who take ownership, thrive on challenge, and want to grow with us.

What we offer

  • Competitive salary + Virtual Stock Options (VSOPs)
  • Autonomy & responsibility from day one
  • Remote-friendly & flexible work environment
  • Your own gear + access to modern tools
  • Extras like Wellpass
  • Real-world impact - accelerate how Europe finances and transforms its residential building stock

Hiring process

  • Brief async intro / short call
  • Team interview (skills & mindset)
  • Case or challenge
  • Founder chat & offer

We move fast - and give clear feedback.

Ready to help transform buildings into better places to live, own, and finance? Let’s talk.#### About us

At credium, we’re on a mission to make owning the new renting - by building the digital infrastructure that powers simple and sustainable homeownership.

We enable mortgage banks, property owners, and real estate professionals to understand buildings better and faster - from floor plans to energy efficiency, from renovation potential to solar readiness.

We're a VC-funded startup with major banks as customers and a purpose that matters.