Distinguished Architect, AI
As a Distinguished Architect, AI, you will be a technical multiplier for leading AI labs and AI-native companies, bridging their infrastructure aspirations with Datadog's technology roadmap. You will ensure the platform solves unique observability challenges for training and deploying foundational models at scale.
About the job
What You’ll Do:
- Leadership: Demonstrate thought leadership in the AI/LLM space. Influence key decision makers and stakeholders by connecting technical capabilities to organizational and business impact.
- Advisory: Strategically partner with highly technical Founders, Heads of Infrastructure, and Research Lead peers. Guide them on best practices and emerging industry trends in the AI/LLM space. Lead high-level technical and architectural conversations around AI adoption.
- Presentations: Lead deep-dive architecture reviews and design engagements with customer teams and their leaders to share industry trends, best practices, and demonstrate how Datadog can support high-throughput hyper scale AI workloads.
- GTM: Identify emerging AI-native technology shifts and feed them directly back to Datadog Product Management. Co-create custom observability integrations and solutions alongside Product SAs to keep Datadog at the absolute forefront of the AI stack.
- Collaboration: Collaborate with Product Solutions Architecture (PSA), Sales, Sales Engineering and Marketing in providing high-quality technical resources to a broad audience of practitioners and economic buyers.
- Hiring: Assist leadership in recruiting and hiring of top talent for the Product Solutions Architecture and Field CTO teams.
Who You Are:
- Industry Experience: 10+ years of experience with at-scale distributed systems architecture, high-performance computing, or large-scale infrastructure. Deep familiarity with the AI/LLM ecosystem, accelerator hardware (GPUs/TPUs), and modern orchestration frameworks.
- Strategic Thinker: Able to think long term and creatively about a wide variety of technical and business challenges
- Stakeholder Management: Proven experience interacting with and influencing elite individual contributors, research scientists, and technical founders in flat, rapid-growth environments.
- Technologist: A true close-to-the-metal technologist who maintains deep hands-on credibility and can white-board architectural solutions seamlessly with senior engineers. Strong understanding of best practices and real world challenges AI/LLM Ops and LLM Observability (LLMO).
- Presenter: Excellent customer-facing presentation skills for large audiences, comfortable discussing complex technical details as well as with briefing executives or non-technical personas.
- Communicator: Excellent verbal and written communication skills, ability to link product functionality to business objectives, value realization and ROI.
- Travel: Able to travel via auto, train or air up to 50% of the time
- Education: Bachelor’s degree in engineering or related field, Master’s degree preferred
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
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: $300,000—$484,000 USD
Skills
Distributed Systems Architecture, High-Performance Computing, Large-Scale Infrastructure, Ai/Llm Ecosystem, GPU, Tpu, Orchestration Frameworks, Ai/Llm Ops, Llm Observability
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