Customer Success Manager

Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.

At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent blog posts sharing our high-level results for text models and image-text models.

We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data.

This role is based in Redwood City, CA. We are in office 4 days a week.

As our first Customer Success Manager, you will own the post-sales relationship for our growing base of AI customers. You'll work directly with ML engineers, research teams, and infrastructure leads at companies pushing the frontier of model training, helping them realize the full value of our platform: faster training cycles, better model performance, and meaningfully reduced compute costs. This role sits at the intersection of technical depth, strategic account management, and customer advocacy. You'll be a key voice in shaping how DatologyAI scales its customer-facing function as we move from early traction to repeatable growth.

• Own onboarding, adoption, and expansion for a portfolio of enterprise accounts, ensuring customers achieve measurable outcomes with DatologyAI's platform (training speed, model quality, compute savings)

• Build and operationalize the CS function from scratch: playbooks, health scoring frameworks, QBR cadences, and escalation processes

• Serve as the primary relationship owner and project lead post-sale, orchestrating onboarding timelines, technical implementation milestones, and cross-functional coordination across Sales, Research, and Engineering to get customers to value quickly

• Translate complex customer feedback into actionable product insights, serving as a direct line between customers and our technical teams

• Identify expansion opportunities within existing accounts and collaborate with Sales on upsell and renewal motions

• Monitor customer health signals proactively and develop strategies to drive retention and reduce churn risk

• Represent DatologyAI at customer onsite meetings, technical reviews, and strategic business reviews

• 4+ years in Customer Success, Technical Account Management, or a related post-sale function (ideally data infrastructure or AI/ML company)

• Proven experience managing highly technical enterprise customers. You can hold a substantive conversation with ML engineers and data scientists about model training, data pipelines, and infrastructure tradeoffs

• A track record of building CS programs from the ground up

• Strong commercial instincts: you think about retention, expansion, and NRR

• Excellent communication skills. You can translate technical complexity for executive audiences and business value for engineering teams

• Comfort operating in ambiguity and moving quickly in a small, high-ownership environment

• Prior experience as an early CS hire at an early-stage company

• Familiarity with the enterprise AI/ML landscape