Sr. AI Scientist

Our Warehouse Execution Software leverages advances in classical and modern optimization techniques to bring intelligent execution to the world of intralogistics and warehouse automation.

We synchronize discrete and low-level logistics related processes to create a real-time decision engine that drives labor and equipment at the highest efficiency.

Our software provides customers the operational agility they need to efficiently handle the demands of an Omni-channel environment.

We are looking for a highly motivated individual who can develop cutting edge algorithms using domain knowledge from Machine Learning, Computer Vision, and Operations Research.

The candidate should not only have a solid grasp of theoretical approaches but also a practical mindset regarding the tradeoffs between solution complexity versus optimality, emerging versus proven techniques, and coding from scratch versus utilizing existing frameworks.

Many of the problems we encounter are novel and have never been solved before, so creative, out-of-the-box thinking and a fondness for experimentation are a must.

We also want someone who stays current with recent trends in AI / ML so our approaches remain the most robust and competitive in the industry.

Finally, the role requires strong team and interdisciplinary collaboration to see products through the development cycle from beginning to end.

What we offer : What We Offer :

What We Offer :

• Career Development
• Competitive Compensation and Benefits
• Pay Transparency
• Global Opportunities

Learn More Here :

Tasks and Qualifications :

This is What You Will do in This Role :

Apply AI principles and technologies to solve and / or improve planning, automation, search, classification, recommendation, prediction, and optimization problems arising from the need to streamline supply chain operations, sometimes involving synthesis of components in production.

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 from building pipelines for on-site acquisition, experimenting with datasets that are used to train models powering data-driven solutions.

Identify and develop model-centric solutions architecture.

Develop model-based POCs leveraging advances in GPU, Sensor Fusion, Edge and Cloud Computing technologies for performance increase of high-automation intralogistics systems.

Design and build statistical forecasting and machine learning models.

Support the matter experts in commercial AI and autonomy solutions delivery.

Learn and apply new tools, technologies, and industry best practices.

What We are Looking For :

Master’s or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, Operations Research, Machine Learning, Control Engineering, Industrial Engineering, or related field.

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.

Background in Perception, Machine Vision, Robotics, or related fields.

Experience with Python / C++, ROS, and widely used AI / ML SDKs on edge devices.

5+ yrs developing AI / ML solutions under Agile methodologies and CI / CD.

5+ yrs practicing asset control and documentation Git, Confluence.

Familiarity with some of the following AI and Data Science frameworks : Pandas, PyTorch, Tensorflow / Keras, MXNET, Scikit-learn, Matplotlib, Numpy, fast.

ai, Tensorboard, Ignite, Neptune, etc.

5+ years of experience, including academic experience, in any of the above.

5+ yrs delivering models into production CNNs, LSTMs, Transformers, Ensemble Classifiers, etc.

5 yrs usage of CUDA, PyTorch, TensorFlow or similar.

Experience identifying and working with applicable key open-source SDKs, frameworks, and tools.

Ability to accommodate some domestic and international travel.

Ways to Stand Out

PhD in Machine Learning, Mathematics, Statistics, Physics, Engineering, or equivalent experience.

Ability to communicate with stakeholder audiences of various seniority levels and backgrounds.

Experience with deploying ML models on the edge for real time applications, and edge lifecycle management

Proficiency with NVIDIA’s edge technologies (CUDA, Deepstream, & TensorRT SDKs.)

Proficiency with containerization & orchestration tools like Docker, Kubernetes, Helm, Terraform

Proficiency in Unity, Nvidia Omniverse or similar simulation engines.

Natural inclination for and / or record of leadership and engineering best-practices test-driven development, patterns, conventions, and delivery standards.

Familiarity with Robotics, PLC programming, Predictive Maintenance, or Reinforcement Learning is a plus.