Product Manager II - Model Lab
DatadogNew York, New York, USAPosted 10 March 2026
Job Description
As a Product Manager II for Model Lab , you will define and launch Datadog’s experiment tracking platform built for teams training and fine-tuning foundational models. Model Lab centralizes metrics, hyperparameters, datasets, code versions, artifacts, and lineage to help ML and AI teams achieve reliable, reproducible, and explainable training runs at scale.
This is a 0→1 opportunity to build a new product that will be deeply integrated into Datadog’s observability platform. You will shape the product vision, validate the market, and drive execution for a new category-defining offering that serves AI research, ML platform, and applied AI teams.
At Datadog, we value our office culture — the relationships it builds, the creativity it fosters, and the collaboration that comes from working together. We operate as a hybrid workplace to help employees create a work-life harmony that fits their needs.
What You'll Do:
Define the vision and strategy for Model Lab, establishing Datadog’s position in experiment tracking and model training observability
Lead 0→1 product discovery with AI research teams, ML platform engineers, and infrastructure leaders to deeply understand experiment tracking workflows and pain points
Design a system that unifies training metrics, hyperparameters, artifacts, dataset lineage, and model evaluation into a coherent and scalable experience
Identify differentiation opportunities vs. competitive alternatives and homegrown internal tooling
Partner closely with engineering and design to ship foundational capabilities such as experiment lineage, artifact versioning, distributed training visibility, and reproducibility workflows
Collaborate with go-to-market teams to define packaging, positioning, and early customer programs
Drive early customer adoption, including design partnerships and private beta programs
Translate ambiguous, research-oriented workflows into clear product primitives and APIs
Establish success metrics and iterate quickly based on user feedback and usage data
Who You Are:
You have 4+ years of Product Management experience, with at least one 0→1 product or major feature launch
You are entrepreneurial and comfortable operating with ambiguity, defining problems before defining solutions
You have experience with developer tools, ML infrastructure, data platforms, or AI/LLM systems
You understand (or can quickly ramp on) modern ML training workflows — distributed training, experiment tracking, hyperparameter tuning, artifact storage, evaluation pipelines
You have familiarity with frameworks such as PyTorch, TensorFlow, JAX, or distributed training frameworks
You are opinionated about product quality and developer experience, especially around APIs, SDKs, and workflows
You can translate highly technical concepts into compelling product narratives
You are comfortable making prioritization tradeoffs in fast-paced, competitive markets
You thrive in cross-functional environments and can align engineering, design, and GTM around a clear strategy
You are excited to build in a rapidly evolving AI ecosystem
Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. If you’re passionate about building technical products and want to shape the future of AI infrastructure, we encourage you to apply.
Benefits and Growth:
New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
Continuous professional development, product training, and career pathing
Intradepartmental mentor and buddy program
An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
Access to Inclusion Talks, our Internal panel discussions
Global mental health benefits for employees and dependents age 6+
Competitive global benefits
Benefits and Growth listed above may vary based on the country of employment and the nature of employment with Datadog.
Datadog offers a competitive salary and equity p ... (truncated, view full listing at source)
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