Job description

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 Willing to travel for multi-day onsites with customer engineering teams and for industry and research conferences Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: Best-in-breed onboarding Generous global benefits Intra-departmental mentor and buddy program for in-house networking New hire stock equity (RSUs) and employee stock purchase plan (ESPP) Continuous professional development, product training, and career pathing An inclusive company culture, able to join our Community Guilds and Inclusion Talks Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan. The reasonably estimated yearly salary for this role at Datadog is: $149,000 — $198,000 USD About Datadog: Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram , LinkedIn, and Datadog Learning Center. Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference. Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form . This form is for accommodation requests only and cannot be used to inquire about the status of applications. Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice . For information on our AI policy, please visit Interviewing at Datadog AI Guidelines .

Requirements

Must-haves

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.
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
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
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
Willing to travel for multi-day onsites with customer engineering teams and for industry and research conferences
Best-in-breed onboarding
Generous global benefits
Intra-departmental mentor and buddy program for in-house networking
New hire stock equity (RSUs) and employee stock purchase plan (ESPP)

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aigomlawsazuregcpkubernetesaccessibility

Platform: greenhouse
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Benefits

Benefits detected in the listing.

Dental

401(K)

Paid Time Off

Professional Development