• Core Job Responsibilities:
  • Frame and solve variety of intralogistics and planning problems with advanced analytics and AI techniques.
  • See solution through full development cycle from inception, to proof of concept, to MVP, to final product deployment. Ensure what is delivered meets business requirements.
  • Work directly with real and synthetic data to train models and build data-driven solutions.
  • Design and build simulation tools of intralogistics processes to train and test models.
  • Design and build statistical forecasting and machine learning models.
  • Learn and apply new tools, technologies, and industry best practices.

Key Qualifications

• Master's or PhD in Computer Science, Artificial Intelligence, Operations Research, Applied Mathematics, Control Engineering, Industrial Engineering, or equivalent field.
• Fluency in at least one general purpose programming language. Python or Java preferred. Statistical or database languages also a plus: R, MATLAB, SQL, etc.
• Knowledge of Reinforcement Learning / Approximate Dynamic Programming: MDP, Monte Carlo, MCTS, TD, Dyna-Q, online vs. offline learning, exploration strategies (epsilon-greedy, optimistic initial values, UCB1, etc.), DQN, DDQN, Dueling DQN, DDPG, REINFORCE, A2C, A3C, PPO, TRPO, SAC, MARL.
• Knowledge of Control Theory: Optimal control, MPC, LGQ, Adaptive Control
• Knowledge of standard Machine Learning models and techniques: Linear regression, logistic regression, decision trees, SVM, kNN, ensemble learning, XGBoost.
• Knowledge of Deep Learning architectures and use cases: ANN, CNN, RNN, VAE.
• Familiarity with some of the following AI and Data Science frameworks: Pandas, PyTorch, Tensorflow/Keras, MXNET, Scikit-learn, Matplotlib, Numpy, fast.ai, Tensorboard, Ignite, Weights & Biases, etc.
• 2 years of experience, including academic experience, in any of the above.

Ways to Stand Out

• Experience with RL Frameworks: OpenAI Gym, Dopamine, RLLib, OpenAI Baselines, Stable Baselines, Garage, Coach, etc.
• Experience with simulation and modeling tools: AnyLogic, Arena, Panda3D, Simio, SimPy, etc.
• Familiarity with any software IDE: PyCharm, IntelliJ, Visual Studio, Jupyter Notebook, etc.
• Familiarity with Cloud Computing: GCP, Azure, AWS, Docker, Kubernetes, edge computing
• Other software engineering skills: Git, Anaconda, OOP, test-driven design, common design patterns, dependency management, and build tools.

Dematic is committed to supporting your continued professional growth. We offer training specifically aimed at your personal development and tailored to your individual job requirements. In addition to a great work environment, we offer a competitive compensation & benefits package.