Staff AI Engineer - Notebooks
DatadogNew York, New York, USAPosted 24 February 2026
Job Description
We're looking for a Staff Software Engineer with deep experience in AI/ML to join the Notebooks team. Datadog Notebooks provide a flexible tool for ad-hoc data analysis, collaborative investigations, and technical documentation. Its fluidity makes it an ideal surface for agents and humans to interact. The Notebook team is building AI-driven workflows for natural-language data analysis, assisted graph generation, automated investigations, and more. You will act as a technical leader in this domain, focused on building industry-leader AI-powered features. We’re looking for a product-minded ML engineer with strong technical expertise, excellent communication skills, and a track record of driving impactful initiatives end to end.
At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.
What You’ll Do:
Spearhead AI initiatives within Notebooks, serving as the technical owner for the design, development, and deployment of ML/AI-driven features.
Shape the long-term technical direction and strategy for AI workflows within Notebooks and related products.
Champion the full development cycle: from rapid prototyping and defining success metrics/evaluations to running experiments, iterating, and achieving production-ready scale and reliability.
Drive product direction with a strong user focus, always advocating for the best end-user experience.
Coordinate with other leaders and product teams interested in leveraging Notebooks in their AI-powered workflows.
Develop robust agent systems, including tools, retrieval and planning strategies, guardrails, prompt management/evaluations, fallbacks, and human-in-the-loop processes.
Stay up to date with industry trends, guiding the adoption of new technologies and evolving best-practices.
Who You Are:
You have a BS/MS/PhD in a scientific field or equivalent experience
10+ years of relevant engineering experience, as well as experience acting as a technical lead
Proven track record of leading large-scale AI/ML initiatives in a product-driven environment
You have significant experience in model deployment, development, training, fine-tuning, and evaluation
You are passionate about mentoring others and cultivating a high-performing engineering culture.
You’re excited about leveraging AI tools to enhance how you code, solve problems, and build
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:
Generous and competitive benefits package
New hire stock equity (RSUs) and employee stock purchase plan
Continuous career development and pathing opportunities
Employee-focused best in class onboarding
Internal mentor and cross-departmental buddy program
Friendly and inclusive workplace culture
Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.
#LI-Hybrid
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:
$234,000
$300,000 USD
About Datadog:
Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity ... (truncated, view full listing at source)
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