Here’s what you’ll be doing:

• Model Development & Research: Designing, implementing, and optimizing deep learning models using PyTorch for various tasks (e.g., computer vision, natural language processing, time series analysis).
• Pipeline & Infrastructure: Building robust and scalable machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Performance Optimization: Identifying and resolving performance bottlenecks in model training and inference, leveraging techniques like distributed training and hardware acceleration.
• Experimentation & Analysis: Conducting rigorous experimentation, analyzing results, and iterating on models to achieve state-of-the-art performance.
• Collaboration & Mentorship: Working closely with data scientists, ML engineers, and other stakeholders. Providing technical leadership and mentoring to junior team members.
• Code Quality: Writing clean, well-documented, and production-ready code, adhering to best practices for reproducibility and maintainability.
• Staying Current: Keeping abreast of the latest research, trends, and advancements in deep learning and the PyTorch ecosystem.

What you'll bring to the table:

• 5+ years of professional experience in machine learning or deep learning, with a significant focus on PyTorch.
• Strong proficiency in Python and its scientific computing ecosystem (NumPy, pandas, scikit-learn).
• Deep understanding of various deep learning architectures (e.g., CNNs, RNNs, Transformers, GANs) and their applications.
• Experience with data preprocessing, feature engineering, and data augmentation techniques for deep learning.
• Familiarity with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, DVC, Kubeflow).
• Experience with cloud platforms (e.g., AWS SageMaker, Google Cloud AI Platform, Azure ML) for training and deploying models.
• Solid understanding of mathematical foundations of machine learning and deep learning (linear algebra, calculus, probability, statistics).
• Experience with version control systems (Git).
• Excellent problem-solving, analytical, and communication skills.

Bonus points for:

• Experience with other deep learning frameworks (e.g., TensorFlow, JAX).
• Knowledge of distributed training techniques and tools (e.g., PyTorch Lightning, Horovod).
• Experience with specialized hardware (e.g., GPUs, TPUs) and optimizing code for them.
• Contributions to open-source machine learning projects or relevant publications.
• Familiarity with MLOps platforms and tools.