Senior Sales Engineer - AI Accounts

As a Senior Sales Engineer on Datadog's AI Accounts team, you'll serve as the technical lead for a small portfolio of the world's leading AI labs — accounts that today represent a meaningful share of Datadog's revenue and are growing faster than any cohort in our business. You'll own the technical relationship end to end, work shoulder-to-shoulder with customer engineering teams in production, and help define how Datadog shows up for a cohort whose buying behavior looks nothing like traditional enterprise.

This team was piloted earlier this year and is now being formalized. You'll be joining early enough to shape the playbook rather than inherit one.

What You'll Do

Own the technical relationship across a small portfolio of AI labs, go deep on how each customer trains, serves, and scales their models (and/or the downstream products and platforms), and become the person their engineers call first

Partner with your AE to drive expansion inside accounts that grow unpredictably and fast: identify new workloads and teams as they spin up, and get in front of them before a need becomes a ticket

Lead discovery inside flat, engineering-led organizations where influence sits with individual contributors rather than titles, and where there are no formal scoping calls to rely on

Run full-scale POVs in production rather than controlled pilots: instrument real workloads at real scale, prove value against the customer's own success criteria, and work through the operational realities of doing that safely

Act as an extension of the customer’s engineering/observability team rather than an external operator - this requires judgment about which standard gates serve the customer and which to break

Work Slack-native and asynchronously as the default, with the responsiveness and proactive posture these accounts expect: flag new features, bugs, and incidents before they ask

Bring product and engineering in early and often, and close the loop fast — leadership needs near-real-time signal from this cohort, and you'll be a primary source of it

Deliver technical narratives that land with engineers who pay for capability first and rationalize cost second - savings is a downstream conversation, not an opener

Build reusable assets and patterns from each engagement so the next lab onboards faster than the last

Keep activity, deal progress, and customer context documented and current

Who You Are

7+ years in Sales Engineering, Solutions Engineering, or a comparable technical customer-facing role; equivalent depth gained inside an AI lab, AI infrastructure company, or ML platform team is equally welcome

Hands-on depth in the AI/ML infrastructure stack: GPU fleet management, distributed training, inference serving, model and agent evaluation, and the observability challenges specific to each

Strong foundation in public cloud (AWS, Azure, GCP), Kubernetes, APM and distributed tracing, and modern, agentic observability practice and usage patterns (i.e. MCP)

Comfortable in flat organizations - able to engage an IC and a founder in the same week and calibrate to both without reaching for hierarchy

Biased toward doing: you'd rather solve a customer's problem and explain the shortcut afterward than ask permission first, and you have the judgment to know where that line sits

Thrive in ambiguity and unpredictable growth - scoping, timelines, and account trajectories will change under you, repeatedly

Actively coachable. The strongest people we hire ask for a dry run, take the feedback, and visibly change what they do on the next attempt

Persistent about access. Getting time with people operating at this pace is hard, and you treat that as an interesting problem rather than a demoralizing one

Demonstrated capability using AI tooling in your own workflow and scaling it to others — credibility with this cohort depends on being a practitioner, not an observer

Experience mentoring peers or contributing to team enablement