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Deep Learning Engineer
About The Position
Deep Learning Engineer
We are seeking a talented Deep-learning engineer to help us advance our deep-learning capabilities.
The ideal candidate should be a self-driven, motivated, and independent thinker who is passionate about using data and machine learning to drive business outcomes.
Responsibilities:
- Develop and implement cutting-edge deep-learning models and algorithms for demand forecasting applications.
- Conduct research and experimentation to identify and evaluate new approaches for improving model accuracy and performance.
- Collaborate with cross-functional teams, including business stakeholders, data engineers, and software developers, to deploy and maintain machine learning systems in production.
- Mentor and train junior team members, promoting best practices and fostering a culture of continuous learning and improvement.
- Communicate technical findings and insights to non-technical stakeholders, including executives and other decision-makers.
Requirements
Must have:
- 4+ years of hands-on experience in deep learning.
- Degree in Computer Science, Statistics, or related quantitative field, Ph.D. Preferable.
- Experience in leveraging deep learning and machine learning in domains such as finance/trading, reinforcement learning, or natural language processing.
- Experience with DL frameworks for convenience, such as PyTorch Lightning or DL-Catalyst
- Experience building tabular machine learning models using gradient boosting methods and deep learning.
- Strong understanding of machine learning systems in production, including good coding practices for testing, reproducibility, and version control.
- Knowledge of alternative training time model compression techniques as well as large-scale AutoML pipelines for Neural Network Architectural Search
- Excellent written and verbal communication skills.
Nice to have:
- Published papers, patents, or professional posts.
- Experience with MLOps and cloud platforms like GCP.
- Experience with workflow orchestration tools like Apache Airflow or Dagster to schedule and monitor deep-learning workflows.
- Strong data visualization and data analysis skills.
- Knowledge of code optimization, cloud computing, containerization, and continuous integration/continuous deployment (CI/CD) pipelines.
- Competitive programming and data science (Kaggle like) exp