Machine Learning Engineer

Machine Learning Engineer

Requirements

• Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
• 3+ years of professional experience in building information retrieval systems and/or graph-based recommendation systems.
• 3+ years of hands-on professional experience in developing text-based or graph-based machine learning models for production, including data processing, model fine-tuning, model deployment and model evaluation
• 2+ years of professional experience in building services to host machine learning models in production at scale
• 2+ years of professional experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
• 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow, and Sklearn
• 2+ years of professional experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker
• 2+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)
• Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases

Responsibilities

• Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users.
• Apply machine learning techniques including LLMs, knowledge graphs, deep learning including generative models, natural language understanding, topic modeling, GNNs and named entity recognition to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud based machine learning architectures.
• Own the performance, scalability, metric based deployed evaluation, and ongoing data driven enhancements of your products.
• Keep abreast of the latest advancements in NLP research, techniques, and tools and apply this knowledge onto ML Features.

Preferred Qualifications

• Exposure to advanced techniques such as reinforcement learning and graph neural networks
• Standout colleague, strong communication skills, with experience working across functions and teams. Ability to teach, mentor and lead through influence
• Bonus points for relevant PhD and/or machine learning related research publications