Lead Software Engineer (AI & Robotics)

What you will work on

• Own and develop the application. Maintain and extend RobTrack’s software, connecting data management, model training, predictions and user-facing workflows.

• Build and validate applied ML. Develop models and pipelines for process prediction and parameter optimization using experimental and simulation data, and contribute to computer-vision applications.

• Strengthen the engineering foundations. Improve software architecture, testing, data quality, reproducibility and deployment. Develop CI/CD and versioning practices, balancing new capabilities with a maintainable codebase.

• Maintain web and cloud infrastructure. Manage the web interface, server infrastructure and cloud platform environment, including deployments, access management, troubleshooting and ongoing maintenance.

• Connect simulation, HPC and machine learning. Work with our numerical simulation specialists to develop and deploy multiphysics simulation workflows on high-performance computing systems and connect the resulting data with ML and manufacturing applications.

• Turn technical work into industrial results. Collaborate with robotics and manufacturing specialists, propose technical initiatives, plan milestones, and contribute to pilot deployments and collaborative R&D deliverables. Present progress, demonstrate the software and explain technical decisions to partners and customers.

Minimum requirements

• Relevant education or equivalent experience. A bachelor’s degree or higher in computer science, computational science, mathematics, physics, engineering or a related field, or equivalent demonstrated practical experience.

• Strong Python and software engineering skills. Experience building and maintaining structured applications or software packages beyond notebooks and exploratory scripts. Comfortable with Git, versioning, debugging, automated testing and technical documentation.

• Practical machine-learning experience. Experience preparing data, training models and evaluating performance using frameworks such as PyTorch, scikit-learn or equivalent. Familiarity with regression, gradient-boosted models such as XGBoost, and neural networks such as multilayer perceptrons (MLPs). A solid understanding of model validation, overfitting, data leakage and appropriate evaluation metrics.

• Experience connecting models to usable software. Ability to build data-processing pipelines, work with relational databases (preferably PostgreSQL), and integrate model training or inference into an application or engineering workflow.

• Practical infrastructure and deployment skills. Experience deploying and maintaining applications on servers or cloud platforms, including containerization with tools such as Docker and CI/CD workflows. Comfortable with basic server administration, managing access permissions and troubleshooting operational issues.

• Basic scientific and engineering understanding. Familiarity with fundamental concepts in physics or mechanical engineering, and the ability to reason about physical processes, numerical results and modelling assumptions.

• Technical ownership, initiative and independence. Demonstrated ability to take responsibility for a substantial software or applied ML project, navigate an existing codebase, prioritize work and carry improvements through to delivery. A research mindset: you proactively identify opportunities, investigate unfamiliar problems and test ideas systematically.

• Clear communication and presentation skills in English. Ability to explain technical decisions and trade-offs, document your work, and collaborate with colleagues and partners from different backgrounds. Comfortable delivering presentations, product demonstrations and technical pitches to technical and non-technical audiences.

• Based in Europe. You must be based in Europe. This is a remote position with some planned international travel. Applicants who are legally resident in Europe will be prioritized. 3D-Components cannot provide support with obtaining or extending residence permits.

Preferred qualifications

• Advanced technical education. A master’s degree or PhD in a relevant field, particularly involving applied ML, scientific computing or computational engineering.

• Application and API development. Experience developing in Rust, building desktop applications with Tauri or Qt/PySide6, or developing APIs with FastAPI.

• Scientific computing and optimization. Familiarity with numerical and multiphysics simulation, finite element methods, design of experiments, uncertainty quantification or parameter optimization.

• Computer vision and additional ML architectures. Experience with computer-vision models for inspection, object detection or defect detection, particularly YOLO models. Familiarity with graph neural networks (GNNs) or transformers is also valuable.

• Robotics and industrial systems. Experience with ROS 2, sensor data, robotic systems or software integration in a manufacturing environment.

• Google Cloud Platform and web operations. Hands-on experience with GCP, Linux server administration, website hosting, domain and DNS management, and cloud resource and cost monitoring.

• ML infrastructure and high-performance computing. Experience deploying simulation or ML workloads on HPC systems, working with GPUs, tracking experiments, and building reproducible training and model-deployment workflows.

• AI-assisted development and open-source collaboration. Experience using AI coding agents effectively while reviewing, testing and taking responsibility for generated code. Experience working with or contributing to open-source projects is a plus.

• Manufacturing knowledge and engineering tools. Prior knowledge of materials science, welding or metal additive manufacturing, or experience with CAD/CAM tools such as Siemens NX.

• Technical leadership and collaborative R&D. Experience guiding technical priorities in a small team, coordinating with external partners, or contributing to research projects, technical reports and industrial pilots.

Why join us?

How to apply

Applications are accepted only through our website. Use the application form on this page to upload your CV. In the brief introduction field, you can tell us about your experience and include an example of software or an ML system you helped build, explaining your contribution. A GitHub repository, project description or portfolio is welcome.

Following the initial application review, our process consists of two interview rounds:

1. Background and mutual fit. A conversation about your experience, interests, working style and expectations, with an opportunity to learn more about the team and the role.
2. Technical discussion. A deeper discussion of your software engineering and ML experience, problem-solving approach and technical decisions. Before this round, shortlisted candidates will receive a small take-home task with a one-week submission deadline, which we will discuss during the interview. The task will be limited in scope, with the expected effort communicated in advance.