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Lead company-wide operations, build scalable processes, partner with finance, people, legal, IT/InfoSec and compliance, resolve cross-functional issues, and grow an operations team to support rapid global scaling.

The Senior ML Infrastructure Engineer will own and optimize GPU infrastructure for training tabular foundation models, ensuring high efficiency and collaboration with research teams.

As an ML Engineer, you will design sklearn-compatible APIs, manage PyTorch serialization, build preprocessing pipelines, and enhance developer experience in the tabular model ecosystem.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

Lead research on tabular foundation models: invent new methods and tooling, set research direction, build real-world benchmarks and datasets, implement baselines, and create automated agentic pipelines with human-in-the-loop to scale evaluation and production use.

Own and consolidate Prior Labs' partner portfolio, manage the SAP strategic relationship, qualify and generate non-SAP partner-led revenue, sign and run integrations/reseller and co-marketing partnerships, and build partner playbooks, co-sell processes, enablement, and reporting. Start as an individual contributor and scale a partnerships team.

Drive commercial adoption of foundation models for structured data through outbound outreach, sales cycle ownership, and technical engagement. Shape sales strategies and materials while managing key buyer relationships.

As the first Partner Manager, you'll manage day-to-day partnerships, coordinate with teams, structure agreements, and develop new partner relationships to drive growth.

As a Data Scientist, you'll bridge cutting-edge AI models with real-world applications, driving experimentation, customer success, and education while showcasing TFM value through PoCs and workshops.

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.