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CAD AI IoT Engineer / Tinkerer

  • On-site, Remote, Hybrid
    • Zürich, Zürich, Switzerland
  • Software Department

Work on tangible, real-world challenges

Snacks of your choice.

Opportunities to grow your hard and soft skills within a diverse agile team 

Unlimited home office or offsite work flexibility.

Job description

Do you feel like you’re just coding toy problems without making a real impact? Join us for a fulfilling career, where you’ll tackle concretely defined, real-world challenges. At Imnoo, you’ll make a significant impact on automating manufacturing processes.

We’re looking for an experienced CAD/AI/IoT engineers.

Why Join Us?

  • A clear path for career growth in machine learning engineering and data engineering roles.

  • The opportunity to solve real and meaningful challenges in data-intensive manufacturing automation, computer vision, and AI-driven pipelines.

  • A dynamic and flexible work environment supporting remote software development.

  • Opportunities for professional development, certification support in AWS, Azure, and MLOps.

  • A platform to share your ideas and opinions, where they are highly valued in our startup tech team.

Your Playground

  • Design and implement advanced data extraction, feature engineering, processing, and pipeline orchestration solutions for handling CAD, 2D, 3D, and large-scale batch data (filtered/unfiltered) with a focus on ML applications like deep learning models and predictive analytics.

  • Own services end-to-end, from proof of concept to production-ready solutions in high-load environments with scalability testing and performance tuning.

  • Maintain and enhance optimization algorithms, machine learning services, and neural network integrations within data pipelines.

  • Improve 2D/3D/CAD tools and solutions through automated, data-driven workflows, including geometric modeling, simulation tools, and GPU acceleration with CUDA.

(Middlemen such as recruiting agencies are not welcome and will be automatically disqualified)

Job requirements

Best to Have: | Essential Skills for Big Data ML Engineer Roles

  • Strong software / coding skills in Python development, C# .NET programming, and C++ expertise with a passion for machine learning, deep learning, and process automation.

  • 5+ years of experience in dynamically typed (e.g., Python scripting) and statically typed languages (e.g., C# backend, C++ systems programming).

  • CAD system development/work experience

  • Strong problem-solving skills for building efficient, scalable data pipelines and ML workflows under production constraints.

  • Foundations or experience in 3D/geometry processing, game development engines (Unity/Unreal), fluid simulations, real-time rendering, CUDA GPU programming, or similar technologies to handle complex big data analytics and spatial data.

Nice to Have: | Preferred Qualifications for ML Pipeline Developers

  • Educational background in mathematics, statistics, or computer science with strong dedication and experience in applied technologies like applied ML and data science (nice to have).

  • CAD data processing experience, including STEP/IGES formats (nice to have).

  • Industry/Mechanical experience in CNC machining, robotics automation, and related fields (optional).

  • Hands-on experience in Frontend development (e.g., Angular, React) and Backend engineering (Node.js, .NET) (optional).

  • Full-stack development experience with microservices architecture (optional).

  • Familiarity with popular machine learning libraries and deep learning frameworks, such as scikit-learn, PyTorch, TensorFlow, and PyTorch Lightning (nice to have).

  • Experience with ML model industrialization tools, including model quantization, ONNX export, Docker containerization, and serverless deployment (nice to have).

  • Knowledge of MLOps practices, ML pipeline development, and tools like MLflow or Kubeflow (nice to have).

  • Expertise in big data processing, data clustering, anomaly detection, filtering, indexing, and querying large datasets efficiently using Elasticsearch or BigQuery (nice to have).

  • Proficiency in automated model training pipelines and A/B testing deployment (optional).

  • Strategic data analysis and research skills, including statistical modeling, error propagation analysis, and identifying clusters or outliers in high-dimensional data (optional).

  • Experience with cloud platforms, particularly AWS services (S3, Lambda, SageMaker) and Azure Cloud Services (Data Factory, ML Studio) (optional).

  • Strong database skills: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) for data warehousing (optional).

  • Deep understanding of advanced data structures and algorithms for efficient querying (optional).

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