Machine Learning Engineer
August 27
🏢 In-office - Manhattan
Description
• Contribute to the process of transitioning research to product by deploying models to production environments. • Collaborate with researchers and engineers at the company’s partners (clients or academic partners) to deploy the company’s technology within their infrastructure. Be effective representatives of the company and our products to partners and other external collaborators. • Manage artifact tracking for different fine-tuned models and data. • Conduct data pipeline work including use of Apache Spark, Apache Arrow, Pandas, distributed computing for large-scale data processing • Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring • Work on simplification and improvement of codebase abstractions to accelerate research momentum • Possess the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training • Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
Requirements
• Experience with Pytorch or low-level programming is a plus. • Experience with distributed computing frameworks like Apache Spark or Dask • Knowledge of cloud computing platforms like AWS, Google Cloud, or Azure • Familiarity with containerization and orchestration tools such as Docker and Kubernetes
Benefits
• Comprehensive medical, dental, and vision benefits
Apply Now
