Senior Technical Product Manager, Data Acquisition

Autodesk•Portland, OR

1d•$128,000 - $228,690•Hybrid

About The Position

The Generative AI Accelerator (GAIA) is a cross-divisional initiative to develop generative AI capabilities using foundation models trained primarily on Autodesk customer content. Begun in FY24, GAIA has created enormous design datasets, trained generative, multi-modal models, driven platform development, and experimented with innovative user experiences, all within Autodesk's “Trusted AI” governance framework. Now, in Q2FY26, we are adding a technical product manager [TPM] who can direct the evolution and drive the internal adoption of the datasets, tools, and services that GAIA has created for its own use in developing foundation models. Because of the confidential nature of GAIA we are inviting only internal candidates to apply at this time. As a Product Manager within the Data Acquisition function, you will own the vision and strategy for the development of GAIA data products and will be directly responsible for achieving them. You and your engineering counterpart in the Bay Area will together execute on a roadmap that captures and balances the needs of stakeholders primarily, but not exclusively, within GAIA to ensure that Autodesk gets the greatest possible value from this data investment. This position reports to the Head of Data Acquisition within GAIA, located in the Foundation Models team within Autodesk Research. It is a hybrid/remote position with preference for candidates in North America. We anticipate travel being required for this role at least several times per year.

Requirements

5+ years as a Product Manager delivering data or analytics products
Experience defining product vision and strategy
Experience as an analyst or managing an analytics function
Proficient in data modeling
Experience with big data technologies such as Snowflake, Spark, Presto, Hive or similar preferred
Track record of effective communication on data and analytics topics
Experience with governance as it applies to data, machine learning, or artificial intelligence
Excellent interpersonal skills
Fully capable of independent work
Will interact comfortably with data engineers, AI developers, and platform PMs, among others
High tolerance for a complex, intense, and challenging project environment

Nice To Haves

Experience with data acquisition to support the development of ML/AI
Experience with product data and associated data such as usage telemetry
Experience with the use of design data for training or fine-tuning AI models

Responsibilities

Ensure that GAIA has the customer content and supporting analytical views and tools to achieve its goals for the creation of foundation models requiring this data
Help realize additional return on GAIA data investments by customizing our datasets, analytical views, and tools for general use within the company, and making those capabilities and DAQ experience and standards (aka DAQ Center of Excellence) generally available in a responsible fashion via our internal platforms
Identify customer and third party developer workflows that could leverage our analytical capabilities and tools (but, to be clear, not datasets) and influence and partner with non-GAIA PMs to support the delivery of these capabilities to those external parties
Defining requirements, overseeing development prioritization, arranging delivery, and driving adoption of the products in your portfolio, and for creating and guiding a strategy and roadmap for that portfolio as a whole.
In FY27, we anticipate the following production releases for which you would assume responsibility: Global Content Analytics v1 and subsequent releases for internal stakeholders CAM, SIM, and Assemblies base datasets for GAIA use GAIA base geometry datasets for general internal use in AMP Support of exploratory use of content analytics by COO-Data Science and Applied AI Engineering Creation of a product roadmap to guide further releases to follow in FY27
Help evolve our principles and policies for the responsible use of customer content, including driving that evolution proactively where desirable.
In FY27, this work will include: Redefining risk tolerance around the use of customer content from “unknowns” and other customers where our information about the customer type is incomplete Continuing work related to automation of data filtering, data retention, and other elements of on-going data management
Partner closely with GAIA product management and strategy to ensure your product plan for customer content datasets meets GAIA AI development needs; partner likewise with the AI Research team
Own the relationship with industry “granular data” teams, providing the needed input into their roadmaps to ensure long-term alignment with GAIA needs
Partner closely with Trusted AI, assuming responsibility for GAIA input to Trusted AI policies impacting data management and use
Establish a robust partnership with data architects in PSET Product Data and the Fusion and AEC industries
Evolve customer content acquisition from general/opportunistic to targeted/directed by establishing a product strategy based on GAIA research and product needs and by aligning our roadmap to that strategy.
Own and execute on that roadmap.
Ensure that DAQ’s primary products, the industry-specific base datasets, are fit for purpose and that their most important users, GAIA AI dev teams, are leveraging them effectively. To this end, drive programmatic update and enrichment of these datasets and of the enabling analytical abstracts.
Report on this (rather than on the mere existence or growth of base datasets).
Make available, responsibly and in line with known development and research needs, subsets of the base datasets to enable non-GAIA (internal) use of this valuable data for AI development.
Make available, responsibly and in line with a broad range of known and anticipated “content analytics” use cases, the content metadata contained in the analytical abstracts.
Identify data pipelines and tools that DAQ should make available for broader use within GAIA or within the company generally and ensure this happens.
Likewise, identify data management activities occurring downstream from DAQ and evaluate them for possible inclusion in DAQ responsibilities.
Drive improvements in company data and data systems that impact data acquisition and contribute to the development of our data policies and governance along these lines.
Monitor and contribute to the multiple initiatives that are using AI coding to radically reshape our product data.
Create new data acquisition initiatives.
In FY27 we anticipate this including an initiative to begin acquiring semantic design intent data at scale and other context for designs in MFG in particular.