Weights & Biases is hiring a
Post-Sales Machine Learning Engineer, Remote - United States

Summary

The job is for a Machine Learning Engineer in the Customer Success team at Weights & Biases. The role involves helping customers solve real-world problems using machine learning tools, collaborating with advanced ML teams worldwide, and ensuring customer success post-sales by guiding them through onboarding, understanding their technical needs, and influencing product development based on customer feedback.

Requirements

• 3-5 years of relevant experience in a similar role
• Experience using one or more of the following packages: TensorFlow/Keras, PyTorch Lightning
• Strong programming proficiency in Python
• Excellent communication and presentation skills, both written and verbal
• Ability to effectively manage multiple conflicting priorities
• Ability to break down complex problems and resolve them through customer consultation and execution
• Experience with cloud platforms (AWS, GCP, Azure)
• Experience with Linux/Unix

Responsibilities

• Master the implementation of efficient, robust, and reproducible machine learning pipelines for engineering teams using Weights & Biases tools
• Serve as a trusted advisor to new customers by identifying their desired outcomes and communicating W&B’s product best practices
• Conduct product training sessions and workshops that demonstrate best practices and the diverse solutions W&B offers
• Serve as a pivotal link between customers and internal teams by continuously providing customer feedback and advocating for customer needs

Preferred Qualifications

• Proficiency with one or more of the following packages: HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, Ray, any of the GenAI frameworks
• Experience with data engineering, MLOps and tools such as Docker and Kubernetes
• Experience as an ML educator and/or building and executing customer training sessions, product demos and/or workshops at a SaaS company

Benefits

• Flexible time off
• Medical, Dental, and Vision for employees and Family Coverage
• Home office budget with a new high-powered laptop
• Competitive salary and equity
• 12 weeks of Parental leave (U.S. specific)
• 401(k) (U.S. specific)