At Weights & Biases, our mission is to build the best developer tools for AI developers. Weights & Biases is a series C company with $250 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more.
We're hiring a Machine Learning Engineer, Customer Success to help our customers solve difficult, real-world problems and engage in ground-breaking research by using our developer tools in their machine learning pipelines.
In this role, you'll be collaborating closely with the most advanced ML teams worldwide, tackling some of the most challenging ML issues in areas like computer vision, robotics, natural language processing, and beyond. This specific role will be centered on W&B’s engagement with new customers who have recently adopted W&B for their projects. You'll have the chance to work with ML teams across various industries to understand their ML requirements, enhance their ML workflow, integrate W&B into their environment effectively, participate in projects, and educate them on the best practices of our product. In particular, the person in this role will be tasked with guiding new customers through their onboarding process, understanding their technical needs, showcasing how W&B enhances their capabilities, designing and implementing customer adoption strategies, and ensuring a seamless transition and successful use of our product post-sales.
Machine Learning Engineers on our customer success teams play a vital role in the success of our customers at Weights & Biases. You'll collaborate with Sales, Support, Product, and Engineering teams to lead the technical success of our customers after the sales process, acting as the primary source of knowledge and representative to our customers. You will play a key role in customer adoption, partnering with our product team to steer product development based on customer feedback and usage patterns.
This is an ideal opportunity for anyone with machine learning experience who is customer-focused and eager to work with the top ML companies globally, focusing on ensuring customer success and adoption post-sales.
Responsibilities:
- 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
Strong Plus:
- 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
Our Benefits: