Senior Sales Engineer - Key Accounts

Overview

Serve as the technical lead for a small portfolio of leading AI labs on Datadog's AI Accounts team, owning the technical relationship end to end and driving expansion inside fast-growing accounts.

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, using judgment about which standard gates serve the customer and which to break
• Work Slack-native and asynchronously as the default, flagging new features, bugs, and incidents before the customer asks
• Bring product and engineering in early and often, and close the loop fast, providing near-real-time signal from this cohort to leadership
• Deliver technical narratives that land with engineers who pay for capability first and rationalize cost second, treating savings as a downstream conversation
• 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

What you'll need

• 7+ years in Sales Engineering, Solutions Engineering, or a comparable technical customer-facing role; or equivalent depth gained inside an AI lab, AI infrastructure company, or ML platform team
• 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: would rather solve a customer's problem and explain the shortcut afterward than ask permission first, with the judgment to know where that line sits
• Thrive in ambiguity and unpredictable growth, with scoping, timelines, and account trajectories that will change repeatedly
• Actively coachable: asks for a dry run, takes feedback, and visibly changes what they do on the next attempt
• Persistent about access to people operating at this pace
• Demonstrated capability using AI tooling in your own workflow and scaling it to others
• Experience mentoring peers or contributing to team enablement
• Willing to travel for multi-day onsites with customer engineering teams and for industry and research conferences

Details

• Location: California, Illinois, Massachusetts, or New York, USA; remote
• Compensation: reasonably estimated yearly salary of $149,000 to $198,000 USD, plus competitive equity and possible variable compensation
• Travel: multi-day onsites with customer engineering teams and industry/research conferences