Post-Sales Machine Learning Engineer, Customer Success - US (Remote)

March 13

Weights & Biases

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Developer-first MLOps platform.

deep learning • developer tools • machine learning • MLOps

51 - 200

💰 Corporate Round on 2022-05

Description

• Master the implementation of efficient, robust, and reproducible machine learning pipelines for engineering teams, leveraging Weights & Biases tools, to ensure smooth customer onboarding and adoption processes. • Serve as a trusted advisor to new customers by identifying their desired outcomes and clearly communicating W&B’s product best practices, ensuring their success through the effective integration and optimization in the post-sales adoption phase. • Conduct product training sessions and workshops that demonstrate best practices and the diverse solutions W&B offers, aimed at facilitating customer success and adoption post-sales. • Serve as a pivotal link between customers and internal teams by continuously providing customer feedback and advocating for customer needs, thereby directly influencing the product roadmap and enhancements to reflect real user experiences, trends, challenges, and successes.

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 and eagerness to help customers who are primarily users of Python deep learning frameworks and tools be successful • Excellent communication and presentation skills, both written and verbal • Ability to effectively manage multiple conflicting priorities, respond promptly and manage time effectively in a fast-paced, dynamic team environment • 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 • 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 with data pipeline tools • 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 • Truly competitive salary and equity • 12 weeks of Parental leave (U.S. specific) • 401(k) (U.S. specific) • Supplemental benefits may be available depending on your location • Explore benefits by country