Staff Technical Program Manager, Managed Intelligence

End-to-end program delivery: Own multi-quarter release planning, dependency governance, and executive communication across the Managed Inference platform.

Complex, high-risk program management: Drive model version rollouts, inference optimization campaigns, SLA readiness for new GPU hardware, and multi-tenant capacity planning from kickoff through delivery.

Cross-functional alignment: Coordinate across Model Engineering, IaaS, Cloud Foundations, Data Center Operations, and external model providers to keep programs on track and unblocked.

Proactive risk identification: Surface risks across model serving, reliability, capacity constraints, and vendor timelines before they become program-level problems.

Execution frameworks and dashboards: Build lightweight, scalable TPM frameworks suited to Crusoe's pace; maintain real-time execution dashboards and deliver crisp, data-driven executive updates.

Phase 0 planning for model onboarding: Own pre-launch planning for model onboarding on new GPU generations, including firmware and driver readiness, CUDA and ROCm stack validation, and commissioning criteria for inference workloads.

Stakeholder leadership: Drive alignment and push back effectively across engineering, product, and operations leadership -- including highly technical stakeholders who have not previously worked with a TPM.

7+ years of experience as a Technical Program Manager in fast-paced technical environments, with a track record of owning complex programs end-to-end across engineering and product organizations.

LLM inference and model serving knowledge: Working familiarity with batching strategies, quantization approaches, and the tradeoffs that govern latency, throughput, and cost at production scale.

Multi-tenant systems experience: Familiarity with isolation, quota management, and SLA enforcement across concurrent workloads.

Fine-tuning and alignment awareness: Sufficient familiarity with fine-tuning and alignment workflows to govern program timelines, identify technical risks, and coordinate across the teams that own them.

Low-structure execution: Proven ability to build execution models in environments where the process did not yet exist, and make them stick with teams that didn't ask for them.

Executive communication: Exceptional written and verbal communication for delivering clear, data-driven, decision-oriented updates to executive stakeholders.

AI tool integration: Active, daily use of AI tools to improve program execution, risk detection, and communication -- not just personal productivity.

Cross-functional influence: Proven ability to drive alignment across engineering, product, and infrastructure leadership without direct authority, including with highly technical stakeholders.

1+ years of experience working with teams building platforms or services for AI inference and/or training.

Direct experience governing model onboarding programs across GPU generations, including firmware, driver, and stack validation.

Experience coaching or mentoring junior TPMs in a high-growth technical environment.

Exposure to multi-site or globally distributed engineering teams.

Background at a Series D to Series F company or a high-performing team within a hyperscaler focused on AI infrastructure.