Applied Research Engineer

We are looking for an Applied Research Engineer to join our core R&D team, where we design and build advanced decision-support systems for industrial production lines. This role bridges the gap between theoretical research and real-world application: you'll implement and optimize algorithms that directly impact productivity, quality, and safety in our customers' factories.

You'll work closely with data scientists, domain experts, and infrastructure engineers to turn innovative ideas into scalable, production-ready solutions.

Duties and Responsibilities

- Develop and implement algorithms for real-time decision-making and optimization.

- Translate research prototypes and experiments into clean, maintainable, and testable production code.

- Collaborate with data scientists to evaluate model performance, run experiments, and tune parameters.

- Build and maintain modular, scalable components that integrate with our existing recommendation engine.

- Contribute to the development of simulation and testing frameworks for validating algorithmic behavior.

- Work with product and data teams to understand user needs and data constraints.

- Participate in design and code reviews, and help maintain high engineering standards.

Education and Experience

- B.Sc. orM.Sc. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.

- 3+ years of hands-on experience developing algorithms or applied ML systems in production environments.

- Strong coding skills in Python; experience with NumPy, Pandas, and software engineering best practices.

- Solid understanding of data structures, optimization, and algorithm design.

- Experience working with real-world noisy data, preferably from sensors or industrial systems.

- Ability to read academic papers and translate them into practical implementations.

- Strong communication skills and a collaborative mindset.

Other Skills

- Experience with time-series data or control systems.

- Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow).

- Knowledge of data versioning and experiment tracking tools (e.g., MLflow, DVC).

- Experience working with SCADA systems or in manufacturing environments.