AI Solutions Engineer, Post Sales Scale - W&B

<div class="content-intro"><div> <div> <div class="gmail_quote"> <div> <div><span id="m_1770241969069985273m_-2746164444908759431gmail-docs-internal-guid-131e4fb0-7fff-b4e9-ff50-e8cf32449b1b">CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at <a href="http://www.coreweave.com/" target="_blank" data-saferedirecturl="https://www.google.com/url?q=http://www.coreweave.comsource=gmailust=1762613132717000usg=AOvVaw3D-UOhNaqEvF5BEWxjYyAU">www.coreweave.com</a>.</span></div> </div> </div> </div> </div></div><p><strong>What You’ll Do</strong><strong><br></strong> The Field Engineering team at Weights Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).</p> <p><strong>About the Role</strong></p> <p>We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.</p> <h2><strong>Key Responsibilities </strong></h2> <ol> <li>Run 1-to-many onboarding and enablement programs<br>Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.</li> <li>Build reusable technical assets that drive self-serve success<br>Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.</li> <li>Operate the scaled motion using signals and feedback loops<br>Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.</li> </ol> <p><strong>Who You Are</strong></p> <ul> <li>3–5 years of relevant experience in a similar role</li> <li>Strong programming proficiency in Python</li> <li>Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)</li> <li>Familiarity with cloud platforms (AWS, GCP, Azure)</li> <li>Excellent communication and presentation skills, both written and verbal</li> <li>Organized and outcomes-driven: you can run programs, measure impact, and iterate.</li> </ul> <p><strong>Preferred</strong></p> <ul> <li>Experience with GenAI and LLMs</li> <li>Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray</li> <li>Experience with hyperparameter optimization solutions</li> <li>Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes</li> <li>Familiarity with data pipeline tools</li> </ul> <p><strong>Wondering if you’re a good fit?</strong><strong><br></strong> We believe in investing in our people, and value candidates who can bring their own diversified experi ... (truncated, view full listing at source)