General Expertise

• PhD-level experience in relevant fields such as Computer Vision, Machine Learning, AI and Data Engineering.
• Strong ability to conduct independent research and collaborate effectively in a team-oriented environment.
• Proven experience in model workflow, model development, Fine-tuning, and evaluation with large-scale neural networks.
• Proficiency in Python with experience in machine learning frameworks like PyTorch, JAX, or similar.
• Experience working with large codebases on GitHub and distributed computing environments.

Data Processing and Evaluation

• Experience with scalable data engineering toolchains such as SQL, Pandas, NumPy, and FiftyOne for image dataset processing.
• Design and build data and evaluation pipelines and perform training of multi-task deep neural networks.
• Analyze large-scale learning datasets using queries and data processing scripts (analyzing, augmenting, and transforming the data).
• Collaborate with data/annotation engineers to develop automated data pipelines with the FiftyOne App.
• Integrate AI models to automate manual tasks (pre-annotation, active learning tools) while maintaining high data quality.
• Work closely with agronomists and annotation engineers to support new model architectures and dataset requirements.

AI Model Lifecycle and Deployment

• Collaborate with other engineers from product teams to align model development with company goals.
• Ownership of the full model development lifecycle, from data preparation to real-world deployment.
• Develop and implemented novel approaches to model fine-tuning, optimization, and evaluation.
• White-box understanding of deep learning: model details, loss formulations, and training algorithms.
• Design and conduct experiments to better understand model behavior and to identify and mitigate risks.
• Work with cross-functional teams to deploy AI solutions in real-world applications.
• Experience in NN deployment toolchains (ONNX) and techniques such as TensorRT, model quantization/pruning.

Robotics and Simulation

• Experience working with robotic learning systems using simulation tools
• Experience with some physical simulators (e.g. Mujoco, IsaacSim, IsaacLab)
• Passionate about building robot learning systems.

Cloud Infrastructure and AI Scalability

• Experience managing cloud infrastructure with GCP and AWS (Instances, Bucket).
• Familiarity with job scheduling/orchestration tools such as Kubernetes (MLFlow, Kubeflow, ZenML).

AI Safety and Robustness

• Ability to develop novel computer vision models for tasks such as classification, object detection, and segmentation.
• Familiarity with AI safety and robustness challenges in real-world environments (e.g., precision agriculture, real-time applications).
• Understanding of fundamental geometric concepts in computer vision, including perspective transformation and epipolar geometry.
• Thrive in a high pace environment, where solutions are often unclear and require exploration.